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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Al Amin Accounting Software Crack Keygen The Ultimate Guide for Windows Users.md

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-<h1>Al-Amin Accounting Software: A Comprehensive Solution for Your Business Needs</h1>
- <p>If you are looking for a reliable, efficient, and user-friendly accounting software for your business, you might want to consider Al-Amin Accounting Software. Al-Amin Accounting Software is a product of SyrianSoft, a leading software company in the Middle East that has been developing accounting solutions since 1992. Al-Amin Accounting Software is designed to meet the needs of small, medium, and large businesses in various sectors and industries. It offers a range of features and benefits that can help you manage your business operations more effectively and efficiently.</p>
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- <p>In this article, we will explore the features and benefits of Al-Amin Accounting Software, how to download and install it on your computer, how to crack and activate it (and why you shouldn't), and some alternatives to consider. By the end of this article, you will have a better understanding of what Al-Amin Accounting Software can do for your business and how to get started with it.</p>
- <h2>Features and Benefits of Al-Amin Accounting Software</h2>
- <p>Al-Amin Accounting Software is a comprehensive solution that covers various aspects of your business management. It has four main modules: accounting and financial management, inventory and warehouse management, sales and customer relationship management, and human resources and payroll management. Each module has its own features and benefits that can help you streamline your business processes and improve your productivity and profitability. Here are some of the key features and benefits of each module:</p>
- <h3>Accounting and financial management</h3>
- <p>This module helps you manage your accounts, invoices, payments, budgets, etc. with ease and accuracy. Some of the features and benefits of this module are:</p>
- <ul>
-<li>It supports multiple currencies, languages, branches, companies, etc.</li>
-<li>It allows you to create unlimited accounts, sub-accounts, cost centers, etc.</li>
-<li>It enables you to record various types of transactions such as cash receipts, cash payments, bank deposits, bank withdrawals, journal entries, etc.</li>
-<li>It generates various types of invoices such as sales invoices, purchase invoices, service invoices, proforma invoices, etc.</li>
-<li>It tracks your receivables and payables and sends reminders to your customers and suppliers.</li>
-<li>It helps you manage your cash flow and budget by providing cash flow statements, budget reports, variance analysis, etc.</li>
-<li>It integrates with other modules such as inventory, sales, payroll, etc. to provide accurate financial data.</li>
-<li>It produces various types of financial reports such as balance sheet, income statement, trial balance, general ledger, etc.</li>
-</ul>
- <h3>Inventory and warehouse management</h3>
- <p>This module helps you track your stock, purchases, sales, transfers, etc. with ease and accuracy. Some of the features and benefits of this module are:</p>
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- <ul>
-<li>It supports multiple warehouses, locations, units, categories, etc.</li>
-<li>It allows you to create unlimited items, sub-items, batches, serial numbers, etc.</li>
-<li>It enables you to record various types of transactions such as purchase orders, purchase receipts, purchase returns, sales orders, sales deliveries, sales returns, stock transfers, stock adjustments, etc.</li>
-<li>It tracks your inventory levels, costs, prices, margins, etc. and alerts you when your stock is low or high.</li>
-<li>It helps you manage your inventory valuation by using different methods such as FIFO, LIFO, average cost, standard cost, etc.</li>
-<li>It integrates with other modules such as accounting, sales, payroll, etc. to provide accurate inventory data.</li>
-<li>It produces various types of inventory reports such as inventory status, inventory movement, inventory valuation, inventory aging, etc.</li>
-</ul>
- <h3>Sales and customer relationship management</h3>
- <p>This module helps you manage your sales orders, quotations, contracts, customers, etc. with ease and efficiency. Some of the features and benefits of this module are:</p>
- <ul>
-<li>It supports multiple sales channels, markets, segments, etc.</li>
-<li>It allows you to create unlimited customers, sub-customers, contacts, leads, opportunities, etc.</li>
-<li>It enables you to record various types of transactions such as quotations, sales orders, sales contracts, sales deliveries, sales invoices, sales returns, etc.</li>
-<li>It tracks your sales performance by providing sales analysis by customer, product, branch, region, etc. </li>
- <li>It helps you manage your customer relationship by providing customer profile, history, feedback, loyalty, etc. </li>
- <li>It integrates with other modules such as accounting, inventory, payroll, etc. to provide accurate sales data. </li>
- <li>It produces various types of sales reports such as sales summary, sales detail, sales commission, sales forecast, etc. </li>
- </ul>
- <h3>Human resources and payroll management</h3>
- <p>This module helps you manage your employees, salaries, deductions, leaves, etc. with ease and compliance. Some of the features and benefits of this module are:</p>
- <ul>
- <li>It supports multiple branches, departments, positions, grades, etc. </li>
- <li>It allows you to create unlimited employees, sub-employees, dependents, beneficiaries, etc. </li>
- <li>It enables you to record various types of transactions such as attendance, absence, overtime, leave, loan, advance, bonus, penalty, etc. </li>
- <li>It tracks your payroll costs by providing payroll analysis by employee, branch, department, position, grade, etc. </li>
- <li>It helps you manage your payroll compliance by providing tax calculation, social security calculation, insurance calculation, wage protection system (WPS), etc. </li>
- <li>It integrates with other modules such as accounting, inventory, sales, etc. to provide accurate payroll data. </li>
- <li>It produces various types of payroll reports such as payslip, payroll summary, payroll detail, payroll statement, payroll register, etc. </li>
- </ul>
- <h2>How to Download and Install Al-Ameen Accounting Software</h2>
- <p>If you are interested in trying out Al-Ameen Accounting Software for yourself or for your business, you can download it from the official website of SyrianSoft. Here are the steps to download and install Al-Ameen Accounting Software on your computer:</p>
- <h3>System requirements</h3>
- <p>Before downloading Al-Ameen Accounting Software,</p><p><strong><em><u><mark style="background-color:#FFFF00;">make sure that your computer meets the minimum or recommended specifications for running the software.</mark></u></em></strong></p>
- <p>According to the developer's website, the minimum and recommended system requirements for Al-Ameen Accounting Software are as follows:</p>
- | Software | Minimum | Recommended | | --- | --- | --- | | Microsoft SQL Server | 2012 | 2012 or higher | | Microsoft .NET Framework | 4.5.2 | 4.5.2 or higher | | Visual C++ Redistributable for Visual Studio | 2015 | 2015 or higher | | Sentinel Protection Key | Required | Required | | Internet Explorer | 11 | 11 or higher | | Platform Update (Windows 7 SP1 and Windows Server 2008 R2 SP1) | Required | Required | | Hardware | Minimum | Recommended | | --- | --- | --- | | Processor | 1 GHz | 2 GHz or higher | | Memory | 2 GB | 4 GB or higher | | Hard Disk (Free Space) | 500 MB | 1 GB or higher | <h3>Download links</h3>
- <p>To download Al-Ameen Accounting Software, you need to visit the official website of SyrianSoft and register for an account. After logging in, you can access the download page and choose the version that suits your needs. The latest version of Al-Ameen Accounting Software is 9.0 - (900.11), which was released on May 18, 2017. The download package consists of two files: Release_Notes.pdf and V_9_900_16_11.exe. The total size of the package is about 255 MB.</p>
- <h3>Installation steps</h3>
- <p>To install Al-Ameen Accounting Software on your computer, you need to follow these steps:</p>
- <ol>
-<li>Download the two files from the download page and save them in one folder on your hard disk.</li>
-<li>Click the file V_9_900_16_11.exe and an extract window will appear. Click Extract button and wait for the extraction process to finish.</li>
-<li>A new file Ameen.exe will appear in the same folder where you saved the downloaded files. Click this file and the installation wizard will start on your computer.</li>
-<li>Follow the instructions on the screen to complete the installation process. You may need to restart your computer after the installation.</li>
-<li>After restarting your computer, you can launch Al-Ameen Accounting Software from the Start menu or from the desktop shortcut.</li>
-</ol>
- <h2>How to Crack and Activate Al-Ameen Accounting Software</h2>
- <p>If you are wondering how to crack and activate Al-Ameen Accounting Software, we have some bad news for you: it is not possible, and even if it was, it would be illegal and unethical. Here are some reasons why you should not try to crack and activate Al-Ameen Accounting Software:</p>
- <h3>Disclaimer</h3>
- <p>Al-Ameen Accounting Software is a licensed software that requires a valid protection key to run. The protection key is a hardware device that plugs into your computer's USB port and verifies your license with the developer's server. Without the protection key, Al-Ameen Accounting Software will run as a demo version with limited functionality and data entry. Cracking and activating Al-Ameen Accounting Software means bypassing the protection key and using a fake license to run the full version of the software. This is a violation of the terms and conditions of use of Al-Ameen Accounting Software and an infringement of the intellectual property rights of SyrianSoft. By cracking and activating Al-Ameen Accounting Software, you are committing a crime that can result in legal action against you.</p>
- <h3>Risks and consequences</h3>
- <p>Even if you manage to find a way to crack and activate Al-Ameen Accounting Software, you are exposing yourself to various risks and consequences that can harm your computer, your data, and your business. Some of these risks and consequences are:</p>
- <ul>
-<li>You may download malware or viruses that can damage your computer or steal your personal information.</li>
-<li>You may get a corrupted or outdated version of Al-Ameen Accounting Software that can cause errors or crashes.</li>
-<li>You may lose your data or compromise its security by using an unverified source of Al-Ameen Accounting Software.</li>
-<li>You may miss out on important updates, patches, bug fixes, and new features that SyrianSoft provides for its customers.</li>
-<li>You may face technical issues or compatibility problems that SyrianSoft cannot help you with because you are using an illegal version of Al-Ameen Accounting Software.</li>
-<li>You may lose your credibility and reputation as a business owner by using a pirated software that does not comply with professional standards and ethics.</li>
-</ul>
- <h3>Alternatives</h3>
- <p>If you are looking for alternatives to cracking and activating Al-Ameen Accounting Software, you have some options that are legal and ethical. Some of these options are:</p>
- <ul>
-<li>You can buy a legitimate license of Al-Ameen Accounting Software from SyrianSoft or its authorized dealers. This way, you can enjoy all the features and benefits of Al-Ameen Accounting Software without any risk or consequence.</li>
-<li>You can request a free trial of Al-Ameen Accounting Software from SyrianSoft or its authorized dealers. This way, you can test Al-Ameen Accounting Software for a limited period of time before deciding whether to buy it or not.</li>
-<li>You can look for other accounting software that suits your budget and needs. There are many accounting software available in the market that offer different features and prices. You can compare them and choose the one that works best for you.</li>
-</ul>
- <h1>Conclusion</h1>
- <p>In conclusion, Al-Ameen Accounting Software is a comprehensive solution for your business needs that offers various features and benefits that can help you manage your accounting, inventory, sales, and payroll processes more effectively and efficiently. It is easy to download and install on your computer, but it requires a valid protection key to run. Cracking and activating Al-Ameen Accounting Software is not possible, and even if it was, it would be illegal and unethical. You should avoid doing so and look for legal and ethical alternatives instead. We hope this article has given you a clear overview of what Al-Ameen Accounting Software can do for your business and how to get started with it. If you have any questions or comments, please feel free to contact us. We would love to hear from you.</p>
- <h4>Frequently Asked Questions</h4>
- <p>Here are some frequently asked questions about Al-Ameen Accounting Software:</p>
- <ol>
-<li><strong>What is the price of Al-Ameen Accounting Software?</strong><br>The price of Al-Ameen Accounting Software depends on the number of users, modules, and features you need. You can contact SyrianSoft or its authorized dealers for a quotation.</li>
-<li><strong>How can I get support for Al-Ameen Accounting Software?</strong><br>You can get support for Al-Ameen Accounting Software by contacting SyrianSoft or its authorized dealers via phone, email, or online chat. You can also visit their website for online help, tutorials, and FAQs.</li>
-<li><strong>Can I use Al-Ameen Accounting Software on multiple computers?</strong><br>Yes, you can use Al-Ameen Accounting Software on multiple computers as long as they are connected to the same network. You will need one protection key per computer, however.</li>
-<li><strong>Can I customize Al-Ameen Accounting Software according to my needs?</strong><br>Yes, you can customize Al-Ameen Accounting Software according to your needs by using its built-in tools such as report designer, form designer, label designer, etc. You can also request SyrianSoft or its authorized dealers for custom development services if you need more advanced customization.</li>
-<li><strong>Can I integrate Al-Ameen Accounting Software with other software?</strong><br>Yes, you can integrate Al-Ameen Accounting Software with other software by using its built-in tools such as data import/export, data synchronization, web services, etc. You can also request SyrianSoft or its authorized dealers for integration services if you need more complex integration.</li>
-</ol>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/EASEUS Partition Master 6.0.1 Server Edition Portable 64 Bit.md

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-<h1>EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit</h1>
-<p>EASEUS Partition Master is a powerful and easy-to-use partition software that allows you to create, resize, move, merge, split, clone, recover, convert, and manage disk partitions on Windows servers and PCs. It supports various file systems such as FAT32, NTFS, EXT2/EXT3/EXT4, ReFS, exFAT, etc. It also supports MBR and GPT disk styles, dynamic disks and volumes, RAID arrays, SSDs and HDDs, USB drives and memory cards.</p>
-<p>In this article, we will introduce EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, which is a special version of EASEUS Partition Master that can run directly from a USB flash drive or CD/DVD without installation. We will also show you how to use it to perform some common partition operations on your server or PC.</p>
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- <h2>What is EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h2>
-<p>EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit is a portable version of EASEUS Partition Master 6.0.1 Server Edition that can run on any Windows server or PC with a 64-bit processor without installation or activation. It has all the features of EASEUS Partition Master 6.0.1 Server Edition, which include:</p>
-<ul>
-<li>Resize/move partition: You can resize or move any partition on your disk without losing data or rebooting your system.</li>
-<li>Clone disk/partition: You can clone an entire disk or a single partition to another disk or partition for backup or migration purposes.</li>
-<li>Merge/split partition: You can merge two adjacent partitions into one larger partition or split a large partition into two smaller partitions for better disk space management.</li>
-<li>Convert disk/partition: You can convert a disk from MBR to GPT or vice versa without deleting partitions or data. You can also convert a partition from one file system to another without formatting or losing data.</li>
-<li>Recover partition: You can recover deleted or lost partitions from unallocated space or damaged disks with ease.</li>
-<li>Manage dynamic volume: You can create, delete, format, resize, move, extend, shrink, split, merge, change drive letter, set active/inactive, explore properties of dynamic volumes on your disk.</li>
-<li>Partition through command prompts: You can execute partition commands through command prompts for advanced users.</li>
-<li>Repair RAID-5 volume: You can repair corrupted RAID-5 volume by reconstructing the data of the failed member disk.</li>
-</ul>
-<p>EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit is compatible with Windows Server 2003/2008/2012/2016/2019 and Windows XP/Vista/7/8/10 (64-bit only). It supports up to 32 disks and unlimited hard disk capacity.</p>
- <h2>Why use EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h2>
-<p>EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit has some advantages over other partition software:</p>
-<ul>
-<li>It is portable: You can run it from a USB flash drive or CD/DVD without installing it on your system. This is convenient and safe, as you don't need to modify your system or registry settings.</li>
-<li>It is fast: You can perform partition operations in a few minutes or seconds, depending on the size and speed of your disk.</li>
-<li>It is reliable: You can trust EASEUS Partition Master to handle your disk partitions without causing any data loss or system crash.</li>
-<li>It is versatile: You can use EASEUS Partition Master to manage not only basic disks, but also dynamic disks, RAID arrays, SSDs, and external devices.</li>
-<li>It is cost-effective: You can get EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit for free from the official website or some third-party sources. You don't need to pay for a license or subscription fee.</li>
-</ul>
- <h2>How to use EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h2>
-<p>To use EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<ol>
-<li>Download EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit from the official website or some third-party sources. The file size is about 40 MB.</li>
-<li>Extract the downloaded file to a USB flash drive or CD/DVD. You can use any compression software such as WinRAR or 7-Zip to do this.</li>
-<li>Connect the USB flash drive or CD/DVD to the server or PC that you want to manage the disk partitions on.</li>
-<li>Run the EPM.exe file from the USB flash drive or CD/DVD. You will see the main interface of EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit.</li>
-<li>Select the disk or partition that you want to operate on from the disk map or the list on the left panel.</li>
-<li>Right-click on the disk or partition and choose the desired operation from the context menu. You can also use the toolbar buttons or the menu bar options to access the operations.</li>
-<li>Follow the instructions on the screen to complete the operation. You may need to confirm some actions or restart your system depending on the operation.</li>
-</ol>
- <h2>Some common partition operations with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit</h2>
-<p>In this section, we will show you how to perform some common partition operations with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, such as resizing, cloning, merging, splitting, converting, and recovering partitions.</p>
- <h3>How to resize a partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>To resize a partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<ol>
-<li>Select the partition that you want to resize from the disk map or the list on the left panel.</li>
-<li>Right-click on the partition and choose Resize/Move from the context menu.</li>
-<li>In the pop-up window, drag the left or right border of the partition to adjust its size. You can also enter the exact size in MB in the boxes below.</li>
-<li>Click OK to confirm the changes. You will see a pending operation on the bottom panel.</li>
-<li>Click Apply on the toolbar to execute the operation. You may need to restart your system if you are resizing a system partition.</li>
-</ol>
- <h3>How to clone a disk/partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>To clone a disk/partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<ol>
-<li>Select the disk or partition that you want to clone from the disk map or the list on the left panel.</li>
-<li>Right-click on the disk or partition and choose Clone from the context menu.</li>
-<li>In the pop-up window, select the destination disk or partition that you want to clone to. Make sure it has enough space to hold all the data from the source disk or partition.</li>
-<li>Click Next to continue. You can choose to clone the disk or partition sector by sector or adjust the partition layout on the destination disk or partition.</li>
-<li>Click Proceed to start the cloning process. You may need to restart your system if you are cloning a system disk or partition.</li>
-</ol>
- <h3>How to merge partitions with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>To merge partitions with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<p></p>
-<ol>
-<li>Select one of the partitions that you want to merge from the disk map or the list on the left panel.</li>
-<li>Right-click on the partition and choose Merge from the context menu.</li>
-<li>In the pop-up window, select another partition that you want to merge with the first one. The two partitions must be adjacent and have the same file system.</li>
-<li>Click OK to confirm the changes. You will see a pending operation on the bottom panel.</li>
-<li>Click Apply on the toolbar to execute the operation. You may need to restart your system if you are merging a system partition.</li>
-</ol>
- <h3>How to split a partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>To split a partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<ol>
-<li>Select the partition that you want to split from the disk map or the list on the left panel.</li>
-<li>Right-click on the partition and choose Split from the context menu.</li>
-<li>In the pop-up window, drag the slider or enter the size in MB to specify how much space you want to allocate for the new partition.</li>
-<li>Click OK to confirm the changes. You will see a pending operation on the bottom panel.</li>
-<li>Click Apply on the toolbar to execute the operation. You may need to restart your system if you are splitting a system partition.</li>
-</ol>
- <h3>How to convert a disk/partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>To convert a disk/partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<ol>
-<li>Select the disk or partition that you want to convert from the disk map or the list on the left panel.</li>
-<li>Right-click on the disk or partition and choose Convert from the context menu.</li>
-<li>In the pop-up window, choose whether you want to convert a disk from MBR to GPT or vice versa, or convert a partition from one file system to another.</li>
-<li>Click OK to confirm the changes. You will see a pending operation on the bottom panel.</li>
-<li>Click Apply on the toolbar to execute the operation. You may need to restart your system if you are converting a system disk or partition.</li>
-</ol>
- <h3>How to recover a partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>To recover a partition with EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit, you need to follow these steps:</p>
-<ol>
-<li>Select an unallocated space or a damaged disk that contains the deleted or lost partition from the disk map or the list on the left panel.</li>
-<li>Right-click on the unallocated space or the damaged disk and choose Partition Recovery from the context menu.</li>
-<li>In the pop-up window, choose whether you want to perform a quick scan or a deep scan to search for the deleted or lost partition. A quick scan is faster but may not find all the partitions, while a deep scan is slower but more thorough.</li>
-<li>Click Next to start the scanning process. You will see a list of found partitions on the right panel.</li>
-<li>Select the partition that you want to recover and click Proceed to recover it. You can also preview the files on the partition before recovering it.</li>
-<li>Click Apply on the toolbar to execute the operation. You may need to restart your system if you are recovering a system partition.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit is a powerful and easy-to-use partition software that can run directly from a USB flash drive or CD/DVD without installation. It can help you create, resize, move, merge, split, clone, recover, convert, and manage disk partitions on Windows servers and PCs. It supports various file systems, disk styles, dynamic disks and volumes, RAID arrays, SSDs and HDDs, USB drives and memory cards. It is fast, reliable, versatile, and cost-effective. It is a great tool for disk partition management and maintenance.</p>
- <h2>FAQs</h2>
-<h3>Q: How can I get EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>A: You can get EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit for free from the official website or some third-party sources. You can also download it from this link: </p>
- <h3>Q: What are the system requirements for EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
-<p>A: EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit requires a Windows server or PC with a 64-bit processor, at least 512 MB of RAM, and at least 100 MB of free disk space.</p>
- <h3>Q: What are the limitations of EASEUS Partition Master 6.0.1 Server Edition Portable 64 bit?</h3>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/EasyWorship 7 Full Version The Ultimate Solution for Creating and Presenting Worship Media.md

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-<p>EasyWorship 7 is a powerful and easy-to-use software that allows you to create and present worship slides, lyrics, videos, scriptures, and more. With EasyWorship 7, you can design and customize your own media library, schedule and manage your services, and control your presentation from any device. EasyWorship 7 is a great tool for churches, ministries, and worship teams who want to enhance their worship experience and engage their audience.</p>
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-<p>A full version of a software is a complete and unlocked version that has all the features and functions of the original software. A full version of a software usually requires a license key or activation code to use it legally and properly.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Chess Titans Free _HOT_ Download Full Version For Pc.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Dominos Pizza - Food Delivery APK A Must-Have App for Pizza Lovers.md

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-<h1>Domino's APK: How to Order Pizza Online with Ease</h1>
-<p>Do you love pizza? Do you want to order it online from the comfort of your home or office? Do you want to enjoy delicious pizza at affordable prices and fast delivery? If you answered yes to any of these questions, then you need to download Domino's APK on your Android device.</p>
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-<h3>A brief introduction to the app and its features</h3>
-<p>Domino's APK is the official app of Domino's Pizza, one of the most popular pizza chains in the world. With this app, you can order pizza online from your nearest Domino's outlet and get it delivered to your doorstep in no time. You can also customize your pizza with your choice of crust, toppings, cheese, and sauces. You can also order other items from the menu, such as pasta, sandwiches, salads, desserts, drinks, and more.</p>
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-<p>Downloading and installing Domino's APK is very easy and simple. All you have to do is follow these steps:</p>
-<ol>
-<li>Search for Domino's APK or Pizza delivery app on the Google Play Store or Apple App Store and tap on install.</li>
-<li>Wait for the app to download and install on your device.</li>
-<li>Open the app and grant the necessary permissions for location, camera, storage, etc.</li>
-<li>You are ready to order pizza online with Domino's APK.</li>
-</ol>
-<h2>How to Use Domino's APK to Order Pizza Online</h2>
-<h3>How to create an account and log in</h3>
-<p>To use Domino's APK, you need to create an account and log in with your email address or phone number. You can also sign up with your Facebook or Google account. Creating an account will help you save your preferences, address, payment details, and order history. You can also earn rewards points for every order you place with Domino's APK.</p>
-<h3>How to browse the menu and customize your order</h3>
-<p>Once you log in, you can browse the menu by tapping on the categories or using the search bar. You can also filter the menu by price, popularity, or ratings. You can tap on any item you like and see its details, such as ingredients, calories, price, etc. You can also customize your order by adding or removing toppings, cheese, sauces, etc. You can also choose the size and quantity of your order.</p>
-<h3>How to apply coupons and offers</h3>
-<p>Domino's APK offers various coupons and offers that can help you save money on your order. You can find them on the home screen or under the deals section. You can also enter a coupon code manually if you have one. To apply a coupon or offer, simply select it and add it to your cart. You will see the discounted price on your checkout screen.</p>
-<h3>How to track your order and enjoy contactless delivery</h3>
-<p>After placing your order, you can track its status and progress on the app or on the website. You can also call the store or the delivery person if you have any queries or issues. Domino's APK also offers contactless delivery, which means you can get your order delivered without any physical contact with the delivery person. You can choose this option on the app or on the website and pay online. You can also instruct the delivery person to leave your order at a safe place, such as your doorstep, lobby, or gate.</p>
-<h2>Why Choose Domino's APK for Pizza Delivery?</h2>
-<h3>The benefits of ordering from Domino's</h3>
-<p>There are many reasons why you should choose Domino's APK for pizza delivery. Here are some of them:</p>
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-<li>Domino's offers a wide variety of pizzas and other items to suit your taste and budget.</li>
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-<p>Domino's APK has received positive feedback and ratings from its users. The app has a 4.5-star rating on the Google Play Store and a 4.7-star rating on the Apple App Store. Here are some of the reviews from the users:</p>
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-<p>"I love this app. It's easy to use and I can order pizza anytime I want. The delivery is fast and the pizza is always hot and delicious. I also like the coupons and offers that they have. I highly recommend this app to anyone who loves pizza."</p>
-<p>"This app is awesome. It has everything I need to order pizza online. I can customize my pizza, apply coupons, track my order, and enjoy contactless delivery. The app is also very secure and reliable. I have never had any issues with it."</p>
-<p>"This app is amazing. It saves me time and money when I order pizza online. The app is very simple and intuitive to use. I can also earn rewards points for every order and get free pizza and other perks. The app is a must-have for pizza lovers."</p>
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-<p>Domino's APK is not the only pizza delivery app available in the market. There are other apps that offer similar services, such as Pizza Hut, Papa John's, Little Caesars, etc. However, Domino's APK stands out from the rest in terms of quality, speed, convenience, and value. Here is a table that compares Domino's APK with other pizza delivery apps:</p>
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-<tr><th>Pizza Delivery App</th><th>Menu Variety</th><th>Delivery Time</th><th>Customer Satisfaction</th><th>Loyalty Program</th></tr>
-<tr><td>Domino's APK</td><td>High</td><td>30 minutes or less</td><td>100% guarantee</td><td>Piece of the Pie Rewards</td></tr>
-<tr><td>Pizza Hut</td><td>Medium</td><td>40 minutes or more</td><td>No guarantee</td><td>Hut Rewards</td></tr>
-<tr><td>Papa John's</td><td>Low</td><td>45 minutes or more</td><td>No guarantee</td><td>Papa Rewards</td></tr>
-<tr><td>Little Caesars</td><td>Low</td><td>No delivery option</td><td>No guarantee</td><td>No loyalty program</td></tr>
-</table>
- <h2>Conclusion</h2>
-<p>To sum up, Domino's APK is the best pizza delivery app that you can use to order pizza online with ease. It has a wide range of pizzas and other items to choose from, fast and fresh delivery, 100% satisfaction guarantee, and a rewarding loyalty program. It also has a user-friendly and convenient app that makes ordering pizza online a breeze. So, what are you waiting for? Download Domino's APK today and enjoy delicious pizza at your doorstep.</p>
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-<h3>Q1. Is Domino's APK safe and secure?</h3>
-<p>A1. Yes, Domino's APK is safe and secure to use. It uses encryption and other security measures to protect your personal and payment information. It also complies with all the privacy policies and regulations.</p>
-<h <h3>Q2. What are the payment options available on Domino's APK?</h3>
-<p>A2. Domino's APK offers various payment options for your convenience. You can pay online with your credit card, debit card, net banking, UPI, or wallet. You can also pay cash on delivery or use a gift card or voucher.</p>
-<h3>Q3. How can I contact Domino's customer service?</h3>
-<p>A3. Domino's customer service is always ready to help you with any queries or issues you may have. You can contact them by calling the toll-free number 1800-103-6888 or by emailing them at [email protected]. You can also chat with them on the app or on the website.</p>
-<h3>Q4. What are the minimum requirements for Domino's APK?</h3>
-<p>A4. Domino's APK requires an Android device with a minimum of 4.4 version and a minimum of 50 MB of free space. It also requires an internet connection and GPS access to function properly.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download Frozen City Mod APK 1.0.6 for Android - Free Purchase.md

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-<h1>How to Download and Play Clash Royale on Bluestacks</h1>
- <p>Clash Royale is one of the most popular and addictive mobile games in the world. It is a real-time strategy game where you collect cards, build decks, and battle other players online. You can join clans, chat with friends, unlock new cards, and earn chests full of rewards. But what if you want to play Clash Royale on a bigger screen, with better graphics, faster performance, and more control? That's where Bluestacks comes in.</p>
- <p>Bluestacks is the best mobile gaming platform for PC and Mac. It lets you play thousands of Android games on your computer, with full keyboard and mouse support, custom settings, and advanced features. You can also stream your gameplay to Facebook or Twitch, record your screen, take screenshots, and more. With Bluestacks, you can enjoy playing Clash Royale on your PC or Mac like never before.</p>
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- <p>In this article, we will show you how to download and install Bluestacks on your PC or Mac, and how to play Clash Royale on it. Follow these simple steps and get ready to clash!</p>
- <h2>Step 1: Download and install Bluestacks on your PC or Mac</h2>
- <p>The first thing you need to do is to download Bluestacks from its official website. You can choose from different versions of Bluestacks, depending on your operating system and Android preference. For example, you can download Bluestacks 5 for Windows 10 with Android 11, or Bluestacks 5 Nougat 64-bit for Mac. Make sure your PC or Mac meets the minimum system requirements for Bluestacks before downloading it.</p>
- <p>Once you have downloaded the Bluestacks installer, run it and follow the instructions to install it on your PC or Mac. You can choose the default location for installation or change it to a different drive. The installation process may take a few minutes, depending on your internet speed and computer performance.</p>
- <h2>Step 2: Launch Bluestacks and sign in with your Google account</h2>
- <p>After installing Bluestacks, launch it from your desktop or start menu. You will see a window like this:</p>
-<p>How to play Clash Royale PC with Bluestacks Emulator<br />
-Clash Royale Bluestacks script for automating moves<br />
-Download Clash Royale PC for Windows & Mac (May 2023)<br />
-How to get Clash Royale (and other supercell games) on Bluestacks<br />
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-Clash Royale Bluestacks vs other Android emulators<br />
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- <img src="(^3^)" alt="Bluestacks home screen">
- <p>Here, you need to sign in with your Google account to access the Google Play Store and other Google services. If you don't have a Google account yet, you can create one here. Signing in with your Google account will also sync your game progress and purchases across devices.</p>
- <h2>Step 3: Search for Clash Royale in the Google Play Store and install it</h2>
- <p>Now that you have signed in with your Google account, you can search for Clash Royale in the Google Play Store app on Bluestacks. You can find the app icon on the home screen or in the app center. Click on it to open it.</p>
- <p>In the Google Play Store app, type "Clash Royale" in the search bar and hit enter. You will see a list of results like this:</p>
- <img src="(^6^)" alt="Clash Royale search results">
- <p>Click on the first result that says "Clash Royale" by Supercell. This will take you to the game's page in the Google Play Store. Here, you can see more information about the game, such as its description, screenshots, reviews, ratings, etc.</p>
- <p>To install Clash Royale on Bluestacks, click on the green "Install" button. This will start downloading and installing the game on your PC or Mac. The process may take a few minutes, depending on your internet speed.</p>
- <h2>Step 4: Enjoy playing Clash Royale on your PC or Mac with Bluestacks</h2>
- <p>Congratulations! You have successfully installed Clash Royale on Bluestacks. Now you can enjoy playing the game on your PC or Mac with a bigger screen, better graphics, faster performance, and more control. You can also use the Bluestacks features to enhance your gaming experience, such as:</p>
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-<li>Customize your keyboard and mouse controls to suit your play style. You can use the game guide to see the default controls or change them as you wish.</li>
-<li>Use the multi-instance feature to play multiple accounts of Clash Royale at the same time. You can also switch between different instances easily with the multi-instance manager.</li>
-<li>Use the macro feature to record and execute complex actions with a single keystroke. You can also edit and share your macros with other players.</li>
-<li>Use the eco mode to reduce CPU and RAM usage and improve battery life. You can also enable or disable notifications, sound, and background apps.</li>
-</ul>
- <p>With Bluestacks, you can take your Clash Royale gameplay to the next level. You can also explore other games in the Bluestacks app center, such as Clash of Clans, Brawl Stars, PUBG Mobile, and more.</p>
- <h1>Conclusion</h1>
- <p>In this article, we have shown you how to download and play Clash Royale on Bluestacks, the best mobile gaming platform for PC and Mac. We have also explained the benefits of playing Clash Royale on Bluestacks and how to use its features to enhance your gaming experience. We hope you found this article helpful and informative.</p>
- <p>If you are a fan of Clash Royale or any other mobile game, we highly recommend you to try out Bluestacks. It is free, easy, and fun to use. You can download it from here and start playing your favorite games on your PC or Mac today.</p>
- <p>Thank you for reading this article. If you have any questions or feedback, please leave them in the comments section below. We would love to hear from you. Happy clashing!</p>
- <h1>FAQs</h1>
- <h3>Q: Is Bluestacks safe to use?</h3>
-<p>A: Yes, Bluestacks is safe to use. It is a legitimate software that has been downloaded by millions of users worldwide. It does not contain any malware, viruses, or spyware. It also does not access or modify any of your personal data or files.</p>
- <h3>Q: Is Bluestacks free to use?</h3>
-<p>A: Yes, Bluestacks is free to use. You can download and install it on your PC or Mac without paying anything. You can also play any game on it without any limitations or restrictions. However, some games may have in-app purchases or ads that require real money.</p>
- <h3>Q: How do I update Clash Royale on Bluestacks?</h3>
-<p>A: To update Clash Royale on Bluestacks, you need to follow these steps:</p>
-<ol>
-<li>Open the Google Play Store app on Bluestacks.</li>
-<li>Click on the menu icon (three horizontal lines) on the top left corner.</li>
-<li>Select "My apps & games" from the menu.</li>
-<li>Find Clash Royale in the list of installed apps and click on "Update".</li>
-<li>Wait for the update to finish and launch the game.</li>
-</ol>
- <h3>Q: How do I transfer my Clash Royale account from my phone to Bluestacks?</h3>
-<p>A: To transfer your Clash Royale account from your phone to Bluestacks, you need to follow these steps:</p>
-<ol>
-<li>On your phone, open Clash Royale and go to the settings menu (gear icon).</li>
-<li>Select "Link a device" and then "This is the old device".</li>
-<li>Select "I want to link to another device" and then "Android device".</li>
-<li>You will see a code that is valid for two minutes.</li>
-<li>On Bluestacks, open Clash Royale and go to the settings menu (gear icon).</li>
-<li>Select "Link a device" and then "This is the new device".</li>
-<li>Enter the code from your phone and confirm.</li>
-<li>Your account will be transferred to Bluestacks.</li>
-</ol>
- <h3>Q: How do I contact Bluestacks support?</h3>
-<p>A: If you have any issues or problems with Bluestacks, you can contact their support team by following these steps:</p>
-<ol>
-<li>Open Bluestacks and click on the menu icon (three horizontal lines) on the top right corner.</li> <li>Select "Help and Support" from the menu.</li>
-<li>You will see a list of topics and articles that may help you solve your issue.</li>
-<li>If you still need assistance, click on the "Report a Problem" button at the bottom of the page.</li>
-<li>Fill out the form with your name, email, description of the problem, and any attachments.</li>
-<li>Click on the "Submit" button and wait for a response from the Bluestacks support team.</li>
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-<h3>The critical reception and box office performance</h3>
-<p>The movie received mixed to positive reviews from critics and audiences alike. It was praised for its gripping plot, suspenseful twists, powerful performances, and social message. However, it was also criticized for its violence, implausible scenarios, and lack of originality.</p>
-<p>The movie performed above average at the box office, earning ₹177.95 million against a budget of ₹85 million.</p>
-<h2>What is Filmyzilla?</h2>
-<p>Fil <h2>What is Filmyzilla?</h2>
-<p>Filmyzilla is a notorious website that provides free downloads of pirated movies and shows from Bollywood, Hollywood, Tollywood, and other regional film industries. It is one of the most popular and visited websites for movie piracy in India and across the world.</p>
-<h3>A notorious website for pirating movies and shows</h3>
-<p>Filmyzilla has been operating for several years and has a huge collection of movies and shows in various languages, genres, and formats. It uploads the latest releases within hours or days of their theatrical or digital premiere, often in high quality. It also offers old and classic movies, as well as dubbed and subbed versions of foreign movies.</p>
-<p>Filmyzilla is an illegal website that violates the Indian and international laws on copyright and intellectual property rights. It hosts and distributes the pirated content without the permission or consent of the original creators or owners. It also generates revenue from advertisements and pop-ups that may contain malware or viruses.</p>
-<h3>The categories and formats of movies available on Filmyzilla</h3>
-<p>Filmyzilla has a user-friendly interface that allows users to browse and download movies and shows according to their preferences. It has various categories such as:</p>
-<ul>
-<li>Bollywood Movies</li>
-<li>Hollywood Movies</li>
-<li>Hollywood Hindi Dubbed Movies</li>
-<li>South Indian Hindi Dubbed Movies</li>
-<li>Punjabi Movies</li>
-<li>Bengali Movies</li>
-<li>Tamil Movies</li>
-<li>Telugu Movies</li>
-<li>Malayalam Movies</li>
-<li>Marathi Movies</li>
-<li>Gujarati Movies</li>
-<li>Kannada Movies</li>
-<li>Urdu Movies</li>
-<li>Pakistani Movies</li>
-<li>Nepali Movies</li>
-<li>Bhojpuri Movies</li>
-<li>Web Series</li>
-<li>TV Shows</li>
-<li>Awards Shows</li>
-<li>Documentaries</li>
-<li>Anime</li>
-<li>Cartoons</li>
-</ul>
-<p>Filmyzilla also offers different formats and qualities of movies and shows such as:</p>
-<ul>
-<li>MP4</li>
-<li>MKV</li>
-<li>AVI</li>
-<li>WEBM</li>
-<li>3GP</li>
-<li>360p</li>
-<li>480p</li>
-<li>720p</li>
-<li>1080p</li>
-<li>HDRip</li>
-<li>DVDRip</li>
-<li>BluRay</li>
-<li>DVDScr</li> <li>CamRip</li>
-<li>PreDVDRip</li>
-</ul>
-<h3>The latest movies leaked by Filmyzilla</h3>
-<p>Filmyzilla is notorious for leaking the latest movies and shows from various film industries. Some of the recent movies that have been leaked by Filmyzilla are:</p>
-<ul>
-<li>Bell Bottom</li>
-<li>Shershaah</li>
-<li>Bhuj: The Pride of India</li>
-<li>Mimi</li>
-<li>Fast and Furious 9</li>
-<li>Black Widow</li>
-<li>The Suicide Squad</li>
-<li>Jungle Cruise</li>
-<li>Loki</li>
-<li>The Family Man Season 2</li>
-<li>Mirzapur Season 2</li>
-<li>Scam 1992</li>
-<li>Money Heist Season 4</li>
-<li>Extraction</li>
-<li>Tenet</li>
-</ul>
-<h2>How to download Table No. 21 full movie from Filmyzilla?</h2>
-<p>If you are still interested in downloading Table No. 21 full movie from Filmyzilla, you should know that it is not an easy or safe process. You will have to face many risks and challenges along the way, and you may also face legal consequences for your actions. Here are the steps to download the movie from Filmyzilla:</p>
-<h3>The steps to access and download the movie</h3>
-<ol>
-<li>First, you will need a VPN (Virtual Private Network) service to bypass the geo-restrictions and access the Filmyzilla website. A VPN will also protect your online identity and privacy from hackers and trackers.</li>
-<li>Next, you will need to find a working domain name of Filmyzilla, as the website keeps changing its domain name to avoid detection and blocking by the authorities. Some of the common domain names of Filmyzilla are filmyzilla.com, filmyzilla.in, filmyzilla.net, filmyzilla.vip, filmyzilla.pro, filmyzilla.me, filmyzilla.co.in, filmyzilla.live, etc.</li>
-<li>Once you find a working domain name, you will need to enter it in your browser and access the Filmyzilla website. You will see a lot of advertisements and pop-ups on the website, which may redirect you to other websites or download unwanted software on your device. You will have to close them or avoid clicking on them.</li>
-<li>Then, you will need to search for Table No. 21 full movie on the website using the search bar or the categories. You will see a list of results with different formats and qualities of the movie. You will have to choose the one that suits your preference and device compatibility.</li>
-<li>After that, you will need to click on the download link or button of the movie. You may have to go through some verification processes or captcha tests before you can start the download. You may also see some fake download links or buttons that may lead you to other websites or download malware on your device. You will have to be careful and avoid them.</li>
-<li>Finally, you will need to wait for the download to complete and then enjoy watching Table No. 21 full movie on your device.</li>
-</ol> <h3>The risks and challenges of downloading from Filmyzilla</h3>
-<p>Downloading Table No. 21 full movie from Filmyzilla may seem like a convenient and cost-effective option, but it comes with many risks and challenges that may ruin your experience and cause you trouble. Some of the risks and challenges are:</p>
-<ul>
-<li>You may download a corrupted or incomplete file that may not play properly or damage your device.</li>
-<li>You may download a file that contains malware or viruses that may infect your device and compromise your data and security.</li>
-<li>You may face slow download speeds, frequent interruptions, or low-quality videos due to the high traffic and low bandwidth of the website.</li>
-<li>You may expose your online activity and identity to hackers and trackers who may monitor your browsing history, IP address, location, and personal information.</li>
-<li>You may violate the terms and conditions of your internet service provider (ISP) and face penalties such as throttling, suspension, or termination of your service.</li>
-</ul>
-<h3>The legal consequences of movie piracy in India</h3>
-<p>Downloading Table No. 21 full movie from Filmyzilla is not only risky and challenging, but also illegal and punishable by law. Movie piracy is a serious crime in India that violates the Cinematograph Act of 1952, the Information Technology Act of 2000, and the Indian Penal Code of 1860. According to these laws, anyone who downloads, uploads, streams, distributes, or exhibits pirated movies or shows without the authorization of the rightful owners can face the following legal consequences:</p>
-<ul>
-<li>A fine of up to ₹10 lakh or three times the value of the pirated content, whichever is higher.</li>
-<li>A jail term of up to three years.</li>
-<li>A civil lawsuit by the original creators or owners for damages and compensation.</li>
-<li>A criminal case by the government for violating the national interest and security.</li>
-</ul>
-<h2>Why you should avoid downloading Table No. 21 from Filmyzilla?</h2>
-<p>By now, you should have realized that downloading Table No. 21 full movie from Filmyzilla is not worth it. It is a bad idea that will not only harm you, but also the film industry and the artists who work hard to create quality content for you. Here are some reasons why you should avoid downloading Table No. 21 from Filmyzilla:</p>
-<h3>The ethical and moral issues of supporting piracy</h3>
-<p>When you download Table No. 21 full movie from Filmyzilla, you are supporting piracy, which is an unethical and immoral act. Piracy is a form of theft that deprives the original creators and owners of their rightful earnings and recognition. It also disrespects their artistic vision and hard work. By downloading pirated movies, you are encouraging more piracy and discouraging more creativity. You are also depriving yourself of the authentic and enjoyable experience of watching movies in theatres or on legal platforms.</p> <h3>The impact of piracy on the film industry and the artists</h3>
-<p>When you download Table No. 21 full movie from Filmyzilla, you are also affecting the film industry and the artists who depend on it for their livelihood. Piracy causes huge losses to the producers, distributors, exhibitors, and other stakeholders of the film industry. According to a report by Ernst & Young, the Indian film industry lost ₹189.5 billion in 2018 due to piracy. Piracy also affects the quality and quantity of movies that are made, as it reduces the incentive and resources for filmmakers to invest in new projects. Piracy also deprives the artists of their fair share of revenue and appreciation, which may demotivate them and affect their career prospects.</p>
-<h3>The alternatives to watch Table No. 21 legally and safely</h3>
-<p>Instead of downloading Table No. 21 full movie from Filmyzilla, you should opt for legal and safe alternatives to watch the movie. There are many platforms that offer Table No. 21 for online streaming or download at a reasonable price. Some of them are:</p>
-<ul>
-<li>Eros Now: This is the official platform of Eros International, the producer of Table No. 21. You can watch the movie on Eros Now with a subscription plan that starts from ₹49 per month. You can also download the movie for offline viewing on your device.</li>
-<li>YouTube: This is the most popular and accessible platform for watching movies and shows online. You can rent or buy Table No. 21 on YouTube for ₹25 or ₹50 respectively. You can also download the movie for offline viewing on your device.</li>
-<li>Google Play Movies: This is another platform that allows you to rent or buy movies and shows online. You can rent or buy Table No. 21 on Google Play Movies for ₹25 or ₹50 respectively. You can also download the movie for offline viewing on your device.</li>
-<li>Amazon Prime Video: This is one of the leading platforms for streaming movies and shows online. You can watch Table No. 21 on Amazon Prime Video with a subscription plan that starts from ₹129 per month or ₹999 per year. You can also download the movie for offline viewing on your device.</li>
-</ul>
-<p>By choosing these alternatives, you will not only enjoy watching Table No. 21 in high quality and without any interruptions, but also support the film industry and the artists who deserve your respect and admiration.</p>
-<h2>Conclusion</h2>
-<p>Table No. 21 is a thrilling and engaging movie that will keep you hooked till the end. It is a movie that deserves to be watched legally and safely, not illegally and riskily. Downloading Table No. 21 full movie from Filmyzilla is a bad idea that will expose you to many dangers and troubles, as well as harm the film industry and the artists who work hard to entertain you. Therefore, you should avoid downloading Table No. 21 from Filmyzilla and opt for legal and safe alternatives to watch the movie.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Table No. 21 and Filmyzilla:</p>
-<ol>
-<li>Is Table No. 21 based on a true story?</li>
-<p>No, Table No. 21 is not based on a true story, but it is inspired by Article 21 of the Indian Constitution, which talks about the protection of life and personal liberty.</p>
-<li>What is the meaning of Table No. 21?</li>
-<p>Table No. 21 is the name of the game show that Mr. Khan hosts in the movie. It is also a reference to Article 21 of the Indian Constitution, which is violated by Mr. Khan in his quest for revenge.</p>
-<li>What is ragging and why is it an issue in India?</li>
-<p>Ragging is a form of bullying that involves physical, mental, or sexual abuse of new or junior students by senior students in educational institutions. It is an issue in India because it causes many cases of harassment, humiliation, injury, suicide, and murder among students every year.</p>
-<li>How does Filmyzilla get access to new movies?</li>
-<p>Filmyzilla gets access to new movies by using various sources such as camcorders, screen recorders, hacked servers, leaked copies, etc. It then uploads them on its website or shares them with other websites.</p>
-<li>How can I report or block Filmyzilla?</li>
-<p>You can report or block Filmyzilla by contacting your ISP, cybercrime cell, or anti-piracy cell and providing them with the details of the website. You can also use software or extensions that block access to pirated websites.</p>
-</ol> I have completed writing the article as per your instructions. I hope you are satisfied with the quality and content of the article. Here is the final version of the article: <h1>Table No. 21 Full Movie Download Filmyzilla 720p: A Thrilling and Illegal Adventure</h1>
-<p>If you are looking for a movie that will keep you on the edge of your seat, you might be tempted to download Table No. 21 full movie from Filmyzilla, a website that offers free downloads of pirated movies and shows. But before you do that, you should know what you are getting into and why it is not a good idea.</p>
-<h2>What is Table No. 21?</h2>
-<p>Table No. 21 is a 2013 Hindi thriller movie starring Paresh Rawal, Rajeev Khandelwal and Tina Desai. It is named after Article 21 of the Indian Constitution, which talks about the protection of life and personal liberty. The movie touches upon the pertinent social issue of ragging, or bullying in college campuses.</p>
-<h3>A brief summary of the plot</h3>
-<p>The movie follows Vivaan and Siya, a married couple who struggle to make ends meet. They win a trip to Fiji in a lucky draw, where they meet Mr. Khan, a mysterious and charming man who invites them to participate in a live game show called Table No. 21. He tells them that the winner of the game will get a whopping amount of ₹210 million as prize money. The rules are simple: they have to answer eight personal questions truthfully and complete a task related to each question. However, as the game progresses, the questions and tasks become increasingly horrific and reveal dark secrets from their past. They soon realize that they are trapped in a deadly game of survival with no escape.</p>
-<h3>The cast and crew of the movie</h3>
-<p>The movie is directed by Aditya Datt and produced by Eros International. The screenplay is written by Shantanu Ray Chhibber and Sheershak Anand, based on their own story. The music is composed by Gajendra Verma, Neeraj Shridhar and Sachin Gupta.</p>
-<p>The main cast of the movie are:</p>
-<ul>
-<li>Paresh Rawal as Abdul Razaq Khan, the host of the game show</li>
-<li>Rajeev Khandelwal as Vivaan Agasthi, one of the contestants</li>
-<li>Tina Desai as Siya Agasthi, Vivaan's wife and another contestant</li>
-<li>Dhruv Ganesh as Akram Khan, Mr. Khan's son who was ragged by Vivaan and his friends in college</li>
-<li>Asheesh Kapur as Bittoo, one of Vivaan's friends</li>
-<li>Sana Amin Sheikh as Neeti, one of Siya's friends</li>
-<li>Hanif Hilal as Ghouse, Mr. Khan's bodyguard</li>
-</ul>
-<h3>The critical reception and box office performance</h3>
-<p>The movie received mixed to positive reviews from critics and audiences alike. It was praised for its gripping plot, suspenseful twists, powerful performances, and social message. However, it was also criticized for its violence, implausible scenarios, and lack of originality.</p>
-<p>The movie performed above average at the box office, earning ₹177.95 million against a budget of ₹85 million.</p>
-<h2>What is Filmyzilla?</h2>
-<p>Filmyzilla is a notorious website that provides free downloads of pirated movies and shows from Bollywood, Hollywood, Tollywood, and other regional film industries. It is one of the most popular and visited websites for movie piracy in India and across the world.</p>
-<h3>A notorious website for pirating movies and shows</h3>
-<p>Filmyzilla has been operating for several years and has a huge collection of movies and shows in various languages, genres, and formats. It uploads the latest releases within hours or days of their theatrical or digital premiere, often in high quality. It also offers old and classic movies, as well as dubbed and subbed versions of foreign movies.</p>
-<p>Filmyzilla is an illegal website that violates the Indian and international laws on copyright and intellectual property rights. It hosts and distributes the pirated content without the permission or consent of the original creators or owners. It also generates revenue from advertisements and pop-ups that may contain malware or viruses.</p>
-<h3>The categories and formats of movies available on Filmyzilla</h3>
-<p>Filmyzilla has a user-friendly interface that allows users to browse and download movies and shows according to their preferences. It has various categories such as:</p>
-<ul>
-<li>Bollywood Movies</li>
-<li>Hollywood Movies</li>
-<li>Hollywood Hindi Dubbed Movies</li>
-<li>South Indian Hindi Dubbed Movies</li>
-<li>Punjabi Movies</li>
-<li>Bengali Movies</li>
-<li>Tamil Movies</li>
-<li>Telugu Movies</li>
-<li>Malayalam Movies</li>
-<li>Marathi Movies</li>
-<li>Gujarati Movies</li>
-<li>Kannada Movies</li>
-<li>Urdu Movies</li>
-<li>Pakistani Movies</li>
-<li>Nepali Movies</li>
-<li>Bhojpuri Movies</li>
-<li>Web Series</li>
-<li>TV Shows</li>
-<li>Awards Shows</li>
-<li>Documentaries</li>
-<li>Anime</li>
-<li>Cartoons</li>
-</ul>
-<p>Filmyzilla also offers different formats and qualities of movies and shows such as:</p>
-<ul>
-<li>MP4</li>
-<li>MKV</li>
-<li>AVI</li>
-<li>WEBM</li>
-<li>3GP</li>
-<li>360p</li>
-<li>480p</li>
-<li>720p</li>
-<li>1080p</li>
-<li>HDRip</li>
-<li>DVDRip</li>
-<li>BluRay</li>
-<li>DVDScr</li>
-<li>CamRip</li>
-<li>PreDVDRip</li>
-</ul>
-<h3>The latest movies leaked by Filmyzilla</h3>
-<p>Filmyzilla is notorious for leaking the latest movies and shows from various film industries. Some of the recent movies that have been leaked by Filmyzilla are:</p>
-<ul>
-<li>Bell Bottom</li>
-<li>Shershaah</li>
-<li>Bhuj: The Pride of India</li>
-<li>Mimi</li>
-<li>Fast and Furious 9</li>
-<li>Black Widow</li>
-<li>The Suicide Squad</li>
-<li>Jungle Cruise</li>
-<li>Loki</li>
-<li>The Family Man Season 2</li>
-<li>Mirzapur Season 2</li>
-<li>Scam 1992</li>
-<li>Money Heist Season 4</li>
-<li>Extraction</li>
-<li>Tenet</li>
-</ul>
- <h2>How to download Table No. 21 full movie from Filmyzilla?</h2>
- <p>If you are still interested in downloading Table No. 21 full movie from Filmyzilla, you should know that it is not an easy or safe process. You will have to face many risks and challenges along the way, and you may also face legal consequences for your actions. Here are the steps to download the movie from Filmyzilla:</p>
- <h3>The steps to access and download the movie <h3>The steps to access and download the movie</h3>
-<ol>
-<li>First, you will need a VPN (Virtual Private Network) service to bypass the geo-restrictions and access the Filmyzilla website. A VPN will also protect your online identity and privacy from hackers and trackers.</li>
-<li>Next, you will need to find a working domain name of Filmyzilla, as the website keeps changing its domain name to avoid detection and blocking by the authorities. Some of the common domain names of Filmyzilla are filmyzilla.com, filmyzilla.in, filmyzilla.net, filmyzilla.vip, filmyzilla.pro, filmyzilla.me, filmyzilla.co.in, filmyzilla.live, etc.</li>
-<li>Once you find a working domain name, you will need to enter it in your browser and access the Filmyzilla website. You will see a lot of advertisements and pop-ups on the website, which may redirect you to other websites or download unwanted software on your device. You will have to close them or avoid clicking on them.</li>
-<li>Then, you will need to search for Table No. 21 full movie on the website using the search bar or the categories. You will see a list of results with different formats and qualities of the movie. You will have to choose the one that suits your preference and device compatibility.</li>
-<li>After that, you will need to click on the download link or button of the movie. You may have to go through some verification processes or captcha tests before you can start the download. You may also see some fake download links or buttons that may lead you to other websites or download malware on your device. You will have to be careful and avoid them.</li>
-<li>Finally, you will need to wait for the download to complete and then enjoy watching Table No. 21 full movie on your device.</li>
-</ol>
- <h3>The risks and challenges of downloading from Filmyzilla</h3>
-<p>Downloading Table No. 21 full movie from Filmyzilla may seem like a convenient and cost-effective option, but it comes with many risks and challenges that may ruin your experience and cause you trouble. Some of the risks and challenges are:</p>
-<ul>
-<li>You may download a corrupted or incomplete file that may not play properly or damage your device.</li>
-<li>You may download a file that contains malware or viruses that may infect your device and compromise your data and security.</li>
-<li>You may face slow download speeds, frequent interruptions, or low-quality videos due to the high traffic and low bandwidth of the website.</li>
-<li>You may expose your online activity and identity to hackers and trackers who may monitor your browsing history, IP address, location, and personal information.</li>
-<li>You may violate the terms and conditions of your internet service provider (ISP) and face penalties such as throttling, suspension, or termination of your service.</li>
-</ul>
- <h3>The legal consequences of movie piracy in India</h3>
-<p>Downloading Table No. 21 full movie from Filmyzilla is not only risky and challenging, but also illegal and punishable by law. Movie piracy is a serious crime in India that violates the Cinematograph Act of 1952, the Information Technology Act of 2000, and the Indian Penal Code of 1860. According to these laws, anyone who downloads, uploads, streams, distributes, or exhibits pirated movies or shows without the authorization of the rightful owners can face the following legal consequences:</p>
-<ul>
-<li>A fine of up to ₹10 lakh or three times the value of the pirated content, whichever is higher.</li>
-<li>A jail term of up to three years.</li>
-<li>A civil lawsuit by the original creators or owners for damages and compensation.</li>
-<li>A criminal case by the government for violating the national interest and security.</li>
-</ul>
- <h2>Why you should avoid downloading Table No. 21 from Filmyzilla?</h2>
-<p>By now, you should have realized that downloading Table No. 21 full movie from Filmyzilla is not worth it. It is a bad idea that will not only harm you, but also the film industry and the artists who work hard to create quality content for you. Here are some reasons why you should avoid downloading Table No. 21 from Filmyzilla:</p>
- <h3>The ethical and moral issues of supporting piracy</h3>
-<p>When you download Table No. 21 full movie from Filmyzilla, you are supporting piracy, which is an unethical and immoral act. Piracy is a form of theft that deprives the original creators and owners of their rightful earnings and recognition. It also disrespects their artistic vision and hard work. By downloading pirated movies, you are encouraging more piracy and discouraging more creativity. You are also depriving yourself of the authentic and enjoyable experience of watching movies in theatres or on legal platforms.</p>
- <h3>The impact of piracy on <h3>The impact of piracy on the film industry and the artists</h3>
-<p>When you download Table No. 21 full movie from Filmyzilla, you are also affecting the film industry and the artists who depend on it for their livelihood. Piracy causes huge losses to the producers, distributors, exhibitors, and other stakeholders of the film industry. According to a report by Ernst & Young, the Indian film industry lost ₹189.5 billion in 2018 due to piracy. Piracy also affects the quality and quantity of movies that are made, as it reduces the incentive and resources for filmmakers to invest in new projects. Piracy also deprives the artists of their fair share of revenue and appreciation, which may demotivate them and affect their career prospects.</p>
- <h3>The alternatives to watch Table No. 21 legally and safely</h3>
-<p>Instead of downloading Table No. 21 full movie from Filmyzilla, you should opt for legal and safe alternatives to watch the movie. There are many platforms that offer Table No. 21 for online streaming or download at a reasonable price. Some of them are:</p>
-<ul>
-<li>Eros Now: This is the official platform of Eros International, the producer of Table No. 21. You can watch the movie on Eros Now with a subscription plan that starts from ₹49 per month. You can also download the movie for offline viewing on your device.</li>
-<li>YouTube: This is the most popular and accessible platform for watching movies and shows online. You can rent or buy Table No. 21 on YouTube for ₹25 or ₹50 respectively. You can also download the movie for offline viewing on your device.</li>
-<li>Google Play Movies: This is another platform that allows you to rent or buy movies and shows online. You can rent or buy Table No. 21 on Google Play Movies for ₹25 or ₹50 respectively. You can also download the movie for offline viewing on your device.</li>
-<li>Amazon Prime Video: This is one of the leading platforms for streaming movies and shows online. You can watch Table No. 21 on Amazon Prime Video with a subscription plan that starts from ₹129 per month or ₹999 per year. You can also download the movie for offline viewing on your device.</li>
-</ul>
-<p>By choosing these alternatives, you will not only enjoy watching Table No. 21 in high quality and without any interruptions, but also support the film industry and the artists who deserve your respect and admiration.</p>
-<h2>Conclusion</h2>
-<p>Table No. 21 is a thrilling and engaging movie that will keep you hooked till the end. It is a movie that deserves to be watched legally and safely, not illegally and riskily. Downloading Table No. 21 full movie from Filmyzilla is a bad idea that will expose you to many dangers and troubles, as well as harm the film industry and the artists who work hard to entertain you. Therefore, you should avoid downloading Table No. 21 from Filmyzilla and opt for legal and safe alternatives to watch the movie.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about Table No. 21 and Filmyzilla:</p>
-<ol>
-<li>Is Table No. 21 based on a true story?</li>
-<p>No, Table No. 21 is not based on a true story, but it is inspired by Article 21 of the Indian Constitution, which talks about the protection of life and personal liberty.</p>
-<li>What is the meaning of Table No. 21?</li>
-<p>Table No. 21 is the name of the game show that Mr. Khan hosts in the movie. It is also a reference to Article 21 of the Indian Constitution, which is violated by Mr. Khan in his quest for revenge.</p>
-<li>What is ragging and why is it an issue in India?</li>
-<p>Ragging is a form of bullying that involves physical, mental, or sexual abuse of new or junior students by senior students in educational institutions. It is an issue in India because it causes many cases of harassment, humiliation, injury, suicide, and murder among students every year.</p>
-<li>How does Filmyzilla get access to new movies?</li>
-<p>Filmyzilla gets access to new movies by using various sources such as camcorders, screen recorders, hacked servers, leaked copies, etc. It then uploads them on its website or shares them with other websites.</p>
-<li>How can I report or block Filmyzilla?</li>
-<p>You can report or block Filmyzilla by contacting your ISP, cybercrime cell, or anti-piracy cell and providing them with the details of the website. You can also use software or extensions that block access to pirated websites.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download Treasure Mathstorm and Join the Super Solvers in an Amazing Adventure.md

@@ -1,152 +0,0 @@
-<br />
-<h1>How to Download Treasure Mathstorm: A Fun and Educational Game for Kids</h1>
- <p>Do you want to help your kids learn math in a fun and engaging way? Do you want to introduce them to a classic educational game that has entertained and challenged millions of children around the world? If you answered yes, then you should download Treasure Mathstorm, a game that combines math, adventure, and humor in a delightful way.</p>
-<h2>download treasure mathstorm</h2><br /><p><b><b>DOWNLOAD</b> &#187;&#187;&#187; <a href="https://jinyurl.com/2uNUeR">https://jinyurl.com/2uNUeR</a></b></p><br /><br />
- <p>Treasure Mathstorm is an educational game designed for kids ages 6 to 8. It was developed by The Learning Company in 1992 and it is part of the Super Solvers series. In this game, you have to help the elves restore Treasure Mountain by solving math problems and finding treasures. Along the way, you will encounter various obstacles, puzzles, and surprises that will make your journey more exciting.</p>
- <p>In this article, we will tell you everything you need to know about Treasure Mathstorm, including what it is, how to download it, and how to play it. We will also share some tips and tricks to help you get the most out of this game. So, let's get started!</p>
- <h2>What is Treasure Mathstorm?</h2>
- <p>Treasure Mathstorm is an educational game that teaches kids various math skills and concepts in a fun and interactive way. It is suitable for kids who are in grades 1 to 3 or who have a basic knowledge of arithmetic. The game covers topics such as addition, subtraction, multiplication, division, fractions, decimals, time, money, measurement, geometry, logic, and problem-solving.</p>
- <h3>The story and the goal of the game</h3>
- <p>The story of Treasure Mathstorm is that the Master of Mischief, a villain who likes to cause trouble, has invented a machine that changes the weather and freezes Treasure Mountain. He has also hidden all the treasures on the mountain and locked them with math problems. Your goal is to restore the mountain by locating different treasures on the mountain and returning them to the castle at the top. When all the treasures have been restored, the king will have his power back and all of the ice will melt.</p>
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- <h3>The math skills and concepts covered in the game</h3>
- <p>The math skills and concepts covered in Treasure Mathstorm are divided into three levels of difficulty: easy, medium, and hard. You can choose which level you want to play at any time during the game. The math skills and concepts covered in each level are as follows:</p>
- <ul>
-<li>Easy: addition and subtraction up to 18, telling time by hours and half-hours, counting money up to $1.00, identifying shapes and colors.</li>
-<li>Medium: addition and subtraction up to 99, telling time by quarter-hours, counting money up to $5.00, identifying fractions (halves, thirds, fourths), measuring length with inches.</li>
-<li>Hard: addition and subtraction up to 999, telling time by minutes, counting money up to $10.00, identifying fractions (sixths, eighths), measuring length with feet.</li>
-</ul>
- <h3>The features and benefits of the game</h3>
- <p>Treasure Mathstorm has many features and benefits that make it a great educational game for kids. Some of them are:</p>
- <ul>
-<li>It adapts to your child's skill level and progress. The game automatically adjusts the difficulty of the math problems based on your child's performance. It also keeps track of your child's scores and achievements.</li>
-<li>It provides feedback and encouragement. The game gives your child immediate feedback on whether they answered a math problem correctly or incorrectly. It also provides hints and explanations when needed. It also praises your child for their efforts and achievements.</li>
-<li>It offers variety and fun. The game has different types of math problems and activities that keep your child engaged and motivated. It also has colorful graphics, animations, sound effects, and music that make the game more enjoyable.</li>
-<li>It fosters creativity and exploration. The game allows your child to explore the mountain and discover different treasures and surprises. It also lets your child customize their character and their backpack with different items and accessories.</li>
-</ul>
- <h2>How to download Treasure Mathstorm?</h2>
- <p>If you want to download Treasure Mathstorm, you need to make sure that your computer meets the system requirements and compatibility of the game. You also need to find a reliable source and link to download the game. Finally, you need to follow the steps and tips to install and run the game on your computer.</p>
- <h3>The system requirements and compatibility of the game</h3>
- <p>Treasure Mathstorm is an old game that was originally designed for DOS and Windows 3.x operating systems. Therefore, it may not run smoothly on modern computers with newer operating systems such as Windows 10, Mac OS, or Linux. However, there are ways to make the game compatible with your computer by using emulators or virtual machines.</p>
- <p>An emulator is a software that mimics the functions of an old operating system or device on your computer. A virtual machine is a software that creates a separate environment on your computer that runs an old operating system or device. Both methods allow you to run old games and programs on your computer without affecting your main system.</p>
- <p>Some of the popular emulators and virtual machines that you can use to run Treasure Mathstorm are:</p>
- <ul>
-<li>DOSBox: an emulator that runs DOS games and programs on Windows, Mac OS, Linux, and other platforms.</li>
-<li>ScummVM: an emulator that runs games that use the SCUMM engine, such as Treasure Mathstorm.</li>
-<li>VirtualBox: a virtual machine that runs various operating systems such as Windows 3.x, Windows 95, Windows 98, etc.</li>
-<li>VMware: another virtual machine that runs various operating systems such as Windows 3.x, Windows 95, Windows 98, etc.</li>
-</ul>
- <p>You can download these emulators and virtual machines from their official websites or from other trusted sources. You can also find tutorials and guides on how to use them online.</p>
- <h3>The sources and links to download the game</h3>
- <p>Once you have chosen an emulator or a virtual machine to run Treasure Mathstorm, you need to find a source and a link to download the game. There are many websites that offer old games for free or for a small fee. However, not all of them are safe and legal. Some of them may contain viruses, malware, or spyware that can harm your computer or steal your personal information. Some of them may also violate the copyright laws or the terms of service of the original developers or publishers of the game.</p>
- <p>Therefore, you need to be careful and selective when choosing a source and a link to download Treasure Mathstorm. You need to check the reputation and the reviews of the website before downloading anything from it. You also need to scan the downloaded files with an antivirus program before opening them. You also need to respect the rights and the wishes of the original developers or publishers of the game.</p>
- <p>Some of the reputable and legal sources and links to download Treasure Mathstorm are:</p>
- <ul>
-<li>The Learning Company: the original developer and publisher of Treasure Mathstorm. They offer a digital download of the game for $9.99 on their website.</li>
-<li>GOG.com: a digital distribution platform that sells old games that are DRM-free (no copy protection) and compatible with modern systems. They offer Treasure Mathstorm for $5.99 on their website.</li>
-<li>Abandonia: a website that hosts old games that are abandoned by their developers or publishers. They offer Treasure Mathstorm for free on their website.</li>
-</ul>
- <h3>The steps and tips to install and run the game</h3>
- <p>After you have downloaded Treasure Mathstorm from a source and a link of your choice, you need to follow these steps and tips to install and run the game on your computer:</p>
- <ol>
-<li>Extract the downloaded files from the ZIP or RAR archive using a program such as WinZip or WinRAR. </li>
-<li>Create a folder on your computer where you want to store the game files.</li>
-<li>Copy or move the extracted files to the folder you created in step 2.</li>
-<li>Open the emulator or the virtual machine of your choice and configure it according to the instructions and the system requirements of the game.</li>
-<li>Mount or load the game folder or the game file (usually a .exe or a .bat file) on the emulator or the virtual machine and start the game.</li>
-<li>Enjoy playing Treasure Mathstorm!</li>
-</ol>
- <p>Some tips and tricks to help you install and run the game are:</p>
- <ul>
-<li>If you encounter any errors or problems while installing or running the game, you can try to change the settings of the emulator or the virtual machine, such as the memory, the sound, the graphics, etc.</li>
-<li>If you want to save your progress and your scores in the game, you need to create a save file on the emulator or the virtual machine. You can also backup your save file on your computer or on a cloud service.</li>
-<li>If you want to play Treasure Mathstorm with other players online, you can use a program such as DOSBox Daum or DOSBox-X that supports multiplayer mode. You can also use a program such as Hamachi or Tunngle that creates a virtual network for online gaming.</li>
-</ul>
- <h2>How to play Treasure Mathstorm?</h2>
- <p>Now that you have installed and run Treasure Mathstorm on your computer, you are ready to play it. In this section, we will explain how to play Treasure Mathstorm, including the main screen and the menu options of the game, the levels and the challenges of the game, and the rewards and the achievements of the game.</p>
- <h3>The main screen and the menu options of the game</h3>
- <p>The main screen of Treasure Mathstorm is where you can see your character, your backpack, your score, your level, and your time. You can also see the mountain and the castle in the background. You can use your mouse or your keyboard to move your character around and interact with different objects and characters on the screen.</p>
- <p>The menu options of Treasure Mathstorm are located at the top of the screen. You can access them by clicking on them with your mouse or by pressing a key on your keyboard. The menu options are:</p>
- <ul>
-<li>File: where you can start a new game, load a saved game, save your current game, quit the game, or change your player name.</li>
-<li>Options: where you can change the difficulty level of the math problems, turn on or off the music and sound effects, adjust the volume, or view the credits.</li>
-<li>Help: where you can get help on how to play Treasure Mathstorm, how to use DOSBox or ScummVM, or how to contact The Learning Company.</li>
-</ul>
- <h3>The levels and the challenges of the game</h3>
- <p>Treasure Mathstorm has three levels of difficulty: easy, medium, and hard. You can choose which level you want to play at any time during the game. The level you choose affects the type and the number of math problems you have to solve in the game. Treasure Mathstorm has 10 levels of challenges that you have to complete in order to restore the mountain. Each level has a different theme and a different number of treasures to find. The themes and the number of treasures are: - Level 1: Snowy Slopes (10 treasures) - Level 2: Icy Caves (15 treasures) - Level 3: Frozen Forest (20 treasures) - Level 4: Snowman Village (25 treasures) - Level 5: Ice Castle (30 treasures) - Level 6: Crystal Caverns (35 treasures) - Level 7: Blizzard Bluffs (40 treasures) - Level 8: Polar Peak (45 treasures) - Level 9: Cloud City (50 treasures) - Level 10: Treasure Mountain (55 treasures) To complete a level, you have to find all the treasures on that level and return them to the castle at the top of the mountain. To find a treasure, you have to solve a math problem that is attached to it. To return a treasure, you have to carry it to the castle and drop it in the correct bin. The math problems in Treasure Mathstorm are varied and fun. They include: - Addition and subtraction problems that involve snowballs, snowflakes, icicles, etc. - Multiplication and division problems that involve snowmen, penguins, polar bears, etc. - Fraction problems that involve pies, pizzas, cakes, etc. - Decimal problems that involve thermometers, clocks, scales, etc. - Time problems that involve clocks, watches, calendars, etc. - Money problems that involve coins, bills, wallets, etc. - Measurement problems that involve rulers, tapes, scales, etc. - Geometry problems that involve shapes, angles, lines, etc. - Logic problems that involve patterns, sequences, puzzles, etc. - Problem-solving problems that involve word problems, equations, graphs, etc. The math problems in Treasure Mathstorm are not only educational but also entertaining. They have humorous scenarios and characters that make the game more enjoyable. For example: - You have to help a snowman find his missing nose by solving a fraction problem. - You have to help a penguin buy a hat by solving a money problem. - You have to help a polar bear catch a fish by solving a geometry problem. - You have to help a cloud fairy make a rainbow by solving a logic problem. <h3>The rewards and the achievements of the game</h3>
- <p>Treasure Mathstorm has many rewards and achievements that motivate you to play the game and improve your math skills. Some of them are:</p>
- <ul>
-<li>You can earn stars for each math problem you solve correctly. The more stars you earn, the higher your score will be.</li>
-<li>You can earn medals for each level you complete. The medals are bronze, silver, gold, and platinum. The higher the medal, the better your performance on that level.</li>
-<li>You can earn trophies for each level of difficulty you complete. The trophies are easy, medium, and hard. The higher the trophy, the more challenging the math problems you solved.</li>
-<li>You can earn badges for special achievements in the game. The badges are explorer, adventurer, mastermind, super solver, etc. The more badges you earn, the more skills and concepts you mastered.</li>
-<li>You can customize your character and your backpack with different items and accessories that you find or buy in the game. You can also change your character's name and appearance.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Treasure Mathstorm is an educational game that teaches kids math skills and concepts in a fun and interactive way. It is suitable for kids who are in grades 1 to 3 or who have a basic knowledge of arithmetic. The game covers topics such as addition, subtraction, multiplication, division, fractions, decimals, time, money, measurement, geometry, logic, and problem-solving. Treasure Mathstorm is also a fun and engaging game that combines math, adventure, and humor in a delightful way. It has colorful graphics, animations, sound effects, and music that make the game more enjoyable. It also has various obstacles, puzzles, and surprises that make the game more exciting. It also has different types of math problems and activities that keep the game varied and interesting. Treasure Mathstorm is an old game that was originally designed for DOS and Windows 3.x operating systems. Therefore, it may not run smoothly on modern computers with newer operating systems such as Windows 10, Mac OS, or Linux. However, there are ways to make the game compatible with your computer by using emulators or virtual machines. Treasure Mathstorm is a game that you can download from various sources and links online. However, you need to be careful and selective when choosing a source and a link to download the game. You need to check the reputation and the reviews of the website before downloading anything from it. You also need to scan the downloaded files with an antivirus program before opening them. You also need to respect the rights and the wishes of the original developers or publishers of the game. Treasure Mathstorm is a game that you can play by following some steps and tips to install and run the game on your computer. You also need to follow some tips and tricks to help you play the game better and faster. You also need to enjoy playing the game and learning math at the same time. We hope that this article has helped you learn how to download Treasure Mathstorm: a fun and educational game for kids. We also hope that you have fun playing Treasure Mathstorm and improving your math skills. If you have any questions or comments about Treasure Mathstorm or this article, please feel free to contact us or leave a comment below. Thank you for reading! <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Treasure Mathstorm:</p>
- <ol>
-<li>Q: How long does it take to complete Treasure Mathstorm?</li>
-<li>A: It depends on your skill level and your speed. However, it usually takes about 10 to 15 hours to complete all 10 levels of Treasure Mathstorm.</li>
-<li>Q: How can I get more stars, medals, trophies, and badges in Treasure Mathstorm?</li>
-<li>A: You can get more stars by solving more math problems correctly. You can get more medals by completing more levels with higher scores. You can get more trophies by completing more levels of difficulty. You can get more badges by achieving special goals in the game.</li>
-<li>Q: How can I save my progress and my scores in Treasure Mathstorm?</li>
-<li>A: You can save your progress and your scores in Treasure Mathstorm by creating a save file on the emulator or the virtual machine that you are using. You can also backup your save file on your computer or on a cloud service.</li>
-<li>Q: How can I play Treasure Mathstorm with other players online?</li>
-<li>A: You can play Treasure Mathstorm with other players online by using a program such as DOSBox Daum or DOSBox-X that supports multiplayer mode. You can also use a program such as Hamachi or Tunngle that creates a virtual network for online gaming.</li>
-<li>Q: Where can I find more information and resources about Treasure Mathstorm?</li>
-<li>A: You can find more information and resources about Treasure Mathstorm on these websites:</li>
-<ul>
-<li>The Learning Company: the original developer and publisher of Treasure Mathstorm. They offer a digital download of the game for $9.99 on their website.</li>
-<li>GOG.com: a digital distribution platform that sells old games that are DRM-free (no copy protection) and compatible with modern systems. They offer Treasure Mathstorm for $5.99 on their website.</li>
-<li>Abandonia: a website that hosts old games that are abandoned by their developers or publishers. They offer Treasure Mathstorm for free on their website.</li>
-<li>MobyGames: a website that provides information and reviews about old games. They have a page dedicated to Treasure Mathstorm on their website.</li>
-<li>Wikipedia: a free online encyclopedia that provides information about various topics. They have an article about Treasure Mathstorm on their website.</li>
-</ul>
-</ol></p> 197e85843d<br />
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spaces/30SecondsToMoon/30SecondsToMoon/README.md

@@ -1,13 +0,0 @@
----
-title: 30SecondsToMoon
-emoji: 📉
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIFILMS/StyleGANEX/models/mtcnn/mtcnn_pytorch/src/visualization_utils.py

@@ -1,31 +0,0 @@
-from PIL import ImageDraw
-
-
-def show_bboxes(img, bounding_boxes, facial_landmarks=[]):
-    """Draw bounding boxes and facial landmarks.
-
-    Arguments:
-        img: an instance of PIL.Image.
-        bounding_boxes: a float numpy array of shape [n, 5].
-        facial_landmarks: a float numpy array of shape [n, 10].
-
-    Returns:
-        an instance of PIL.Image.
-    """
-
-    img_copy = img.copy()
-    draw = ImageDraw.Draw(img_copy)
-
-    for b in bounding_boxes:
-        draw.rectangle([
-            (b[0], b[1]), (b[2], b[3])
-        ], outline='white')
-
-    for p in facial_landmarks:
-        for i in range(5):
-            draw.ellipse([
-                (p[i] - 1.0, p[i + 5] - 1.0),
-                (p[i] + 1.0, p[i + 5] + 1.0)
-            ], outline='blue')
-
-    return img_copy

spaces/AIWaves/Software_Company/app.py

@@ -1,254 +0,0 @@
-import sys
-
-import os
-from gradio_base import WebUI, UIHelper, PORT, HOST, Client
-from gradio_config import GradioConfig as gc
-from typing import List, Tuple, Any
-import gradio as gr
-import time
-
-class CodeUI(WebUI):
-    
-    def render_and_register_ui(self):
-        self.agent_name:list = [self.cache["agents_name"]] if isinstance(self.cache["agents_name"], str) else self.cache['agents_name']
-        gc.add_agent(self.agent_name)
-    
-    def __init__(
-        self,
-        client_cmd: list,
-        socket_host: str = HOST,
-        socket_port: int = PORT,
-        bufsize: int = 1024,
-        ui_name: str = "CodeUI"
-    ):
-        super(CodeUI, self).__init__(client_cmd, socket_host, socket_port, bufsize, ui_name)
-        self.first_recieve_from_client()
-        self.data_history = list()
-        self.caller = 0
-    
-    def construct_ui(self):
-        with gr.Blocks(css=gc.CSS) as demo:
-            gr.Markdown("""# Agents""")
-            gr.Markdown("""**Agents** is an open-source library/framework for building autonomous language agents.if you want to know more about **Agents**, please check our<a href="https://arxiv.org/pdf/2309.07870.pdf">📄 Paper</a> and<a href="http://www.aiwaves-agents.com/">📦 Github</a>. Here is a demo of **Agents**.""")
-            gr.Markdown("""<font size=5>If an error occurs or the queue is too long, please create your own demo by clicking <font color=red>Duplicate This Space</font> in the upper right corner. Please be patient with building, thank you! It takes about 3-4 minutes.</font>""")
-            with gr.Row():
-                with gr.Column():
-                    self.text_api = gr.Textbox(
-                        value = self.cache["api_key"],
-                        placeholder="openai key",
-                        label="Please input valid openai key for gpt-3.5-turbo-16k."
-                    )
-                    self.radio_mode = gr.Radio(
-                        [Client.SINGLE_MODE],
-                        value=Client.SINGLE_MODE,
-                        interactive=True,
-                        label = Client.MODE_LABEL,
-                        info = Client.MODE_INFO
-                    )
-                    self.chatbot = gr.Chatbot(
-                        elem_id="chatbot1"
-                    )
-                    self.btn_next = gr.Button(
-                        value="Next Agent",
-                        visible=False, elem_id="btn"
-                    )
-                    with gr.Row():
-                        self.text_requirement = gr.Textbox(
-                            value=self.cache['requirement'],
-                            placeholder="Please enter your content",
-                            scale=9,
-                        )
-                        self.btn_start = gr.Button(
-                            value="Start!",
-                            scale=1
-                        )
-                    self.btn_reset = gr.Button(
-                        value="Restart",
-                        visible=False
-                    )
-                
-                with gr.Column():
-                    self.file = gr.File(visible=False)
-                    self.chat_code_show = gr.Chatbot(
-                        elem_id="chatbot1",
-                        visible=False
-                    )   
-                
-                self.btn_start.click(
-                    fn=self.btn_send_when_click,
-                    inputs=[self.chatbot, self.text_requirement, self.radio_mode, self.text_api],
-                    outputs=[self.chatbot, self.btn_start, self.text_requirement, self.btn_reset]
-                ).then(
-                    fn=self.btn_send_after_click,
-                    inputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement],
-                    outputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-                )
-                self.text_requirement.submit(
-                    fn=self.btn_send_when_click,
-                    inputs=[self.chatbot, self.text_requirement, self.text_api],
-                    outputs=[self.chatbot, self.btn_start, self.text_requirement, self.btn_reset]
-                ).then(
-                    fn=self.btn_send_after_click,
-                    inputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement],
-                    outputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-                )
-                self.btn_reset.click(
-                    fn=self.btn_reset_when_click,
-                    inputs=[],
-                    outputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-                ).then(
-                    fn=self.btn_reset_after_click,
-                    inputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement],
-                    outputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-                )
-                self.file.select(
-                    fn=self.file_when_select,
-                    inputs=[self.file],
-                    outputs=[self.chat_code_show]
-                )
-                self.btn_next.click(
-                    fn = self.btn_next_when_click,
-                    inputs=[],
-                    outputs=[self.btn_next]
-                ).then(
-                    fn=self.btn_send_after_click,
-                    inputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement],
-                    outputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-                )
-                
-            self.demo = demo
-    
-
-    def handle_message(self, history:list, state, agent_name, token, node_name):
-        if state % 10 == 0:
-            self.data_history.append({agent_name: token})
-        elif state % 10 == 1:
-            # Same state. Need to add new bubble in same bubble.
-            if len(self.data_history) == 0:
-                self.data_history.append({agent_name:""})
-            self.data_history[-1][agent_name] += token
-        elif state % 10 == 2:
-            # New state. Need to add new bubble.
-            history.append([None, ""])
-            self.data_history.clear()
-            self.data_history.append({agent_name: token})
-        else:
-            assert False, "Invalid state."
-        render_data = self.render_bubble(history, self.data_history, node_name, render_node_name=True)
-        return render_data
-    
-    def btn_send_when_click(self, chatbot, text_requirement, mode, api):
-        """
-        inputs=[self.chatbot, self.text_requirement, radio, text_api],
-        outputs=[self.chatbot, self.btn_start, self.text_requirement, self.btn_reset]
-        """
-        chatbot = [[UIHelper.wrap_css(content=text_requirement, name="User"), None]]
-        yield chatbot,\
-            gr.Button.update(visible=True, interactive=False, value="Running"),\
-            gr.Textbox.update(visible=True, interactive=False, value=""),\
-            gr.Button.update(visible=False, interactive=False)
-        self.send_start_cmd({'requirement': text_requirement, "mode": mode, "api_key": api})
-        return
-    
-    def btn_send_after_click(
-        self, 
-        file,
-        history,
-        show_code,
-        btn_send,
-        btn_reset,
-        text_requirement
-    ):
-        """
-        outputs=[self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-        """
-        if self.caller == 0:
-            self.data_history = list()
-        self.caller = 0
-        receive_server = self.receive_server
-        while True:
-            data_list: List = receive_server.send(None)
-            for item in data_list:
-                data = eval(item)
-                assert isinstance(data, list)
-                state, agent_name, token, node_name = data
-                assert isinstance(state, int)
-                assert state in [10, 11, 12, 99, 98]
-                if state == 99:
-                    # finish
-                    fs = [self.cache['pwd']+'/output_code/'+_ for _ in os.listdir(self.cache['pwd']+'/output_code')]
-                    yield gr.File.update(value=fs, visible=True, interactive=True),\
-                        history, \
-                        gr.Chatbot.update(visible=True),\
-                        gr.Button.update(visible=True, interactive=True, value="Start"),\
-                        gr.Button.update(visible=True, interactive=True),\
-                        gr.Textbox.update(visible=True, interactive=True, placeholder="Please input your requirement", value=""),\
-                        gr.Button.update(visible=False)
-                    return
-                elif state == 98:
-                    yield gr.File.update(visible=False),\
-                        history, \
-                        gr.Chatbot.update(visible=False),\
-                        gr.Button.update(visible=True, interactive=False),\
-                        gr.Button.update(visible=True, interactive=True),\
-                        gr.Textbox.update(visible=True, interactive=False),\
-                        gr.Button.update(visible=True, value=f"Next Agent: 🤖{agent_name} | Next Node: ⭕{node_name}")
-                    return
-                history = self.handle_message(history, state, agent_name, token, node_name)
-                yield gr.File.update(visible=False),\
-                    history, \
-                    gr.Chatbot.update(visible=False),\
-                    gr.Button.update(visible=True, interactive=False),\
-                    gr.Button.update(visible=False, interactive=False),\
-                    gr.Textbox.update(visible=True, interactive=False),\
-                    gr.Button.update(visible=False)
-    
-    def btn_reset_when_click(self):
-        """
-        inputs = []
-        outputs = [self.file, self.chatbot, self.chat_code_show, self.btn_start, self.btn_reset, self.text_requirement, self.btn_next]
-        """
-        return gr.File.update(visible=False),\
-            None, None, gr.Button.update(value="Restarting...", interactive=False),\
-                gr.Button.update(value="Restarting...", interactive=False),\
-                    gr.Textbox.update(value="Restarting", interactive=False),\
-                        gr.Button.update(visible=False)
-    
-    def btn_reset_after_click(
-        self,
-        file,
-        chatbot,
-        show_code,
-        btn_send,
-        btn_reset,
-        text_requirement
-    ):
-        self.reset()
-        self.first_recieve_from_client(reset_mode=True)
-        return gr.File.update(value=None, visible=False),\
-            gr.Chatbot.update(value=None, visible=True),\
-            gr.Chatbot.update(value=None, visible=False),\
-            gr.Button.update(value="Start", visible=True, interactive=True),\
-            gr.Button.update(value="Restart", interactive=False, visible=False),\
-            gr.Textbox.update(value=self.cache['requirement'], interactive=True, visible=True),\
-            gr.Button.update(visible=False)
-        
-    def file_when_select(self, file):
-        CODE_PREFIX = "```python\n{}\n```"
-        with open(file.name, "r", encoding='utf-8') as f:
-            contents = f.readlines()
-        codes = "".join(contents)
-        return [[CODE_PREFIX.format(codes),None]]
-    
-    def btn_next_when_click(self):
-        self.caller = 1             # it will remain the value in self.data_history
-        self.send_message("nothing")
-        time.sleep(0.5)
-        yield gr.Button.update(visible=False)
-        return
-        
-
-if __name__ == '__main__':
-    ui = CodeUI(client_cmd=["python","gradio_backend.py"])
-    ui.construct_ui()
-    ui.run()

spaces/AIZero2Hero4Health/4-ImageSimilaritySearch-SL/app.py

@@ -1,186 +0,0 @@
-from html import escape
-import re
-import streamlit as st
-import pandas as pd, numpy as np
-from transformers import CLIPProcessor, CLIPModel
-from st_clickable_images import clickable_images
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={
-        CLIPModel: lambda _: None,
-        CLIPProcessor: lambda _: None,
-        dict: lambda _: None,
-    },
-)
-def load():
-    model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-    processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
-    df = {0: pd.read_csv("data.csv"), 1: pd.read_csv("data2.csv")}
-    embeddings = {0: np.load("embeddings.npy"), 1: np.load("embeddings2.npy")}
-    for k in [0, 1]:
-        embeddings[k] = embeddings[k] / np.linalg.norm(
-            embeddings[k], axis=1, keepdims=True
-        )
-    return model, processor, df, embeddings
-
-
-model, processor, df, embeddings = load()
-source = {0: "\nSource: Unsplash", 1: "\nSource: The Movie Database (TMDB)"}
-
-
-def compute_text_embeddings(list_of_strings):
-    inputs = processor(text=list_of_strings, return_tensors="pt", padding=True)
-    result = model.get_text_features(**inputs).detach().numpy()
-    return result / np.linalg.norm(result, axis=1, keepdims=True)
-
-
-def image_search(query, corpus, n_results=24):
-    positive_embeddings = None
-
-    def concatenate_embeddings(e1, e2):
-        if e1 is None:
-            return e2
-        else:
-            return np.concatenate((e1, e2), axis=0)
-
-    splitted_query = query.split("EXCLUDING ")
-    dot_product = 0
-    k = 0 if corpus == "Unsplash" else 1
-    if len(splitted_query[0]) > 0:
-        positive_queries = splitted_query[0].split(";")
-        for positive_query in positive_queries:
-            match = re.match(r"\[(Movies|Unsplash):(\d{1,5})\](.*)", positive_query)
-            if match:
-                corpus2, idx, remainder = match.groups()
-                idx, remainder = int(idx), remainder.strip()
-                k2 = 0 if corpus2 == "Unsplash" else 1
-                positive_embeddings = concatenate_embeddings(
-                    positive_embeddings, embeddings[k2][idx : idx + 1, :]
-                )
-                if len(remainder) > 0:
-                    positive_embeddings = concatenate_embeddings(
-                        positive_embeddings, compute_text_embeddings([remainder])
-                    )
-            else:
-                positive_embeddings = concatenate_embeddings(
-                    positive_embeddings, compute_text_embeddings([positive_query])
-                )
-        dot_product = embeddings[k] @ positive_embeddings.T
-        dot_product = dot_product - np.median(dot_product, axis=0)
-        dot_product = dot_product / np.max(dot_product, axis=0, keepdims=True)
-        dot_product = np.min(dot_product, axis=1)
-
-    if len(splitted_query) > 1:
-        negative_queries = (" ".join(splitted_query[1:])).split(";")
-        negative_embeddings = compute_text_embeddings(negative_queries)
-        dot_product2 = embeddings[k] @ negative_embeddings.T
-        dot_product2 = dot_product2 - np.median(dot_product2, axis=0)
-        dot_product2 = dot_product2 / np.max(dot_product2, axis=0, keepdims=True)
-        dot_product -= np.max(np.maximum(dot_product2, 0), axis=1)
-
-    results = np.argsort(dot_product)[-1 : -n_results - 1 : -1]
-    return [
-        (
-            df[k].iloc[i]["path"],
-            df[k].iloc[i]["tooltip"] + source[k],
-            i,
-        )
-        for i in results
-    ]
-
-
-description = """
-# Semantic image search
-**Enter your query and hit enter**
-"""
-
-howto = """
-- Click image to find similar images
-- Use "**;**" to combine multiple queries)
-- Use "**EXCLUDING**", to exclude a query
-"""
-
-
-def main():
-    st.markdown(
-        """
-              <style>
-              .block-container{
-                max-width: 1200px;
-              }
-              div.row-widget.stRadio > div{
-                flex-direction:row;
-                display: flex;
-                justify-content: center;
-              }
-              div.row-widget.stRadio > div > label{
-                margin-left: 5px;
-                margin-right: 5px;
-              }
-              section.main>div:first-child {
-                padding-top: 0px;
-              }
-              section:not(.main)>div:first-child {
-                padding-top: 30px;
-              }
-              div.reportview-container > section:first-child{
-                max-width: 320px;
-              }
-              #MainMenu {
-                visibility: hidden;
-              }
-              footer {
-                visibility: hidden;
-              }
-              </style>""",
-        unsafe_allow_html=True,
-    )
-    st.sidebar.markdown(description)
-    with st.sidebar.expander("Advanced use"):
-        st.markdown(howto)
-
-
-    st.sidebar.markdown(f"Try these test prompts: orange, blue, beach, lighthouse, mountain, sunset, parade")
-    st.sidebar.markdown(f"Unsplash has categories that match: backgrounds, photos, nature, iphone, etc")
-    st.sidebar.markdown(f"Unsplash images contain animals, apps, events, feelings, food, travel, nature, people, religion, sports, things, stock")
-    st.sidebar.markdown(f"Unsplash things include flag, tree, clock, money, tattoo, arrow, book, car, fireworks, ghost, health, kiss, dance, balloon, crown, eye, house, music, airplane, lighthouse, typewriter, toys")
-    st.sidebar.markdown(f"unsplash feelings include funny, heart, love, cool, congratulations, love, scary, cute, friendship, inspirational, hug, sad, cursed, beautiful, crazy, respect, transformation, peaceful, happy")
-    st.sidebar.markdown(f"unsplash people contain baby, life, women, family, girls, pregnancy, society, old people, musician, attractive, bohemian")
-    st.sidebar.markdown(f"imagenet queries include: photo of, photo of many, sculpture of, rendering of, graffiti of, tattoo of, embroidered, drawing of, plastic, black and white, painting, video game, doodle, origami, sketch, etc")
-    
-    
-    _, c, _ = st.columns((1, 3, 1))
-    if "query" in st.session_state:
-        query = c.text_input("", value=st.session_state["query"])
-    else:
-
-        query = c.text_input("", value="lighthouse")
-    corpus = st.radio("", ["Unsplash"])
-    #corpus = st.radio("", ["Unsplash", "Movies"])
-    if len(query) > 0:
-        results = image_search(query, corpus)
-        clicked = clickable_images(
-            [result[0] for result in results],
-            titles=[result[1] for result in results],
-            div_style={
-                "display": "flex",
-                "justify-content": "center",
-                "flex-wrap": "wrap",
-            },
-            img_style={"margin": "2px", "height": "200px"},
-        )
-        if clicked >= 0:
-            change_query = False
-            if "last_clicked" not in st.session_state:
-                change_query = True
-            else:
-                if clicked != st.session_state["last_clicked"]:
-                    change_query = True
-            if change_query:
-                st.session_state["query"] = f"[{corpus}:{results[clicked][2]}]"
-                st.experimental_rerun()
-
-
-if __name__ == "__main__":
-    main()

spaces/ASJMO/freegpt/client/css/settings.css

@@ -1,44 +0,0 @@
-.settings-container {
-	color: var(--colour-2);
-	margin: 24px 0px 8px 0px;
-	justify-content: center;
-}
-
-.settings-container span {
-	font-size: 0.875rem;
-	margin: 0;
-}
-
-.settings-container label {
-	width: 24px;
-	height: 16px;
-}
-
-.settings-container .field {
-	justify-content: space-between;
-}
-
-.settings-container .checkbox input + label,
-.settings-container .checkbox input:checked + label:after {
-	background: var(--colour-1);
-}
-
-.settings-container .checkbox input + label:after,
-.settings-container .checkbox input:checked + label {
-	background: var(--colour-3);
-}
-
-.settings-container .checkbox label:after {
-	left: 2px;
-	width: 10px;
-	height: 10px;
-}
-
-.settings-container .checkbox input:checked + label:after {
-	left: calc(100% - 2px - 10px);
-}
-
-.settings-container .dropdown {
-	padding: 4px 8px;
-	font-size: 0.75rem;
-}

spaces/AchyuthGamer/OpenGPT/client/css/message.css

@@ -1,65 +0,0 @@
-.message {
-	width: 100%;
-	overflow-wrap: break-word;
-	display: flex;
-	gap: var(--section-gap);
-	padding: var(--section-gap);
-	padding-bottom: 0;
-}
-
-.message:last-child {
-	animation: 0.6s show_message;
-}
-
-@keyframes show_message {
-	from {
-		transform: translateY(10px);
-		opacity: 0;
-	}
-}
-
-.message .avatar-container img {
-	max-width: 48px;
-	max-height: 48px;
-	box-shadow: 0.4px 0.5px 0.7px -2px rgba(0, 0, 0, 0.08), 1.1px 1.3px 2px -2px rgba(0, 0, 0, 0.041),
-		2.7px 3px 4.8px -2px rgba(0, 0, 0, 0.029), 9px 10px 16px -2px rgba(0, 0, 0, 0.022);
-}
-
-.message .content {
-	display: flex;
-	flex-direction: column;
-	width: 90%;
-	gap: 18px;
-}
-
-.message .content p,
-.message .content li,
-.message .content code {
-	font-size: 1rem;
-	line-height: 1.3;
-}
-
-@media screen and (max-height: 720px) {
-	.message {
-		padding: 12px;
-		gap: 0;
-	}
-
-	.message .content {
-		margin-left: 8px;
-		width: 80%;
-	}
-
-	.message .avatar-container img {
-		max-width: 32px;
-		max-height: 32px;
-	}
-
-	.message .content,
-	.message .content p,
-	.message .content li,
-	.message .content code {
-		font-size: 0.875rem;
-		line-height: 1.3;
-	}
-}

spaces/Adapter/T2I-Adapter/ldm/data/utils.py

@@ -1,40 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import cv2
-import numpy as np
-from torchvision.transforms import transforms
-from torchvision.transforms.functional import to_tensor
-from transformers import CLIPProcessor
-
-from basicsr.utils import img2tensor
-
-
-class AddCannyFreezeThreshold(object):
-
-    def __init__(self, low_threshold=100, high_threshold=200):
-        self.low_threshold = low_threshold
-        self.high_threshold = high_threshold
-
-    def __call__(self, sample):
-        # sample['jpg'] is PIL image
-        x = sample['jpg']
-        img = cv2.cvtColor(np.array(x), cv2.COLOR_RGB2BGR)
-        canny = cv2.Canny(img, self.low_threshold, self.high_threshold)[..., None]
-        sample['canny'] = img2tensor(canny, bgr2rgb=True, float32=True) / 255.
-        sample['jpg'] = to_tensor(x)
-        return sample
-
-
-class AddStyle(object):
-
-    def __init__(self, version):
-        self.processor = CLIPProcessor.from_pretrained(version)
-        self.pil_to_tensor = transforms.ToTensor()
-
-    def __call__(self, sample):
-        # sample['jpg'] is PIL image
-        x = sample['jpg']
-        style = self.processor(images=x, return_tensors="pt")['pixel_values'][0]
-        sample['style'] = style
-        sample['jpg'] = to_tensor(x)
-        return sample

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/spiralcurve-plugin.d.ts

@@ -1,15 +0,0 @@
-import SpiralCurve from './spiralcurve';
-
-export default class SpiralCurvePlugin extends Phaser.Plugins.BasePlugin {
-    add(
-        config?: SpiralCurve.IConfig
-    ): SpiralCurve;
-
-    add(
-        x?: number, y?: number,
-        startRadius?: number, endRadius?: number,
-        startAngle?: number, endAngle?: number,
-        rotation?: number
-    ): SpiralCurve
-
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/checkbox/Checkbox.js

@@ -1,2 +0,0 @@
-import Checkbox from '../../../plugins/checkbox.js';
-export default Checkbox;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/menu/methods/SetTransitCallbackMethods.js

@@ -1,32 +0,0 @@
-import GetEaseConfig from './GetEaseConfig.js';
-
-var PopUp = function (menu, duration) {
-    menu.popUp(GetEaseConfig(menu.root.easeIn, menu))
-}
-
-var ScaleDown = function (menu, duration) {
-    // Don't destroy here
-    menu.scaleDown(GetEaseConfig(menu.root.easeOut, menu));
-}
-
-export default {
-    setTransitInCallback(callback) {
-        if (callback === undefined) {
-            callback = PopUp;
-        }
-
-        this.transitInCallback = callback;
-        // callback = function(gameObject, duration) {}
-        return this;
-    },
-
-    setTransitOutCallback(callback) {
-        if (callback === undefined) {
-            callback = ScaleDown;
-        }
-
-        this.transitOutCallback = callback;
-        // callback = function(gameObject, duration) {}
-        return this;
-    }
-}

spaces/AlexWang/lama/saicinpainting/evaluation/losses/fid/inception.py

@@ -1,323 +0,0 @@
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision import models
-
-try:
-    from torchvision.models.utils import load_state_dict_from_url
-except ImportError:
-    from torch.utils.model_zoo import load_url as load_state_dict_from_url
-
-# Inception weights ported to Pytorch from
-# http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-FID_WEIGHTS_URL = 'https://github.com/mseitzer/pytorch-fid/releases/download/fid_weights/pt_inception-2015-12-05-6726825d.pth'
-
-
-LOGGER = logging.getLogger(__name__)
-
-
-class InceptionV3(nn.Module):
-    """Pretrained InceptionV3 network returning feature maps"""
-
-    # Index of default block of inception to return,
-    # corresponds to output of final average pooling
-    DEFAULT_BLOCK_INDEX = 3
-
-    # Maps feature dimensionality to their output blocks indices
-    BLOCK_INDEX_BY_DIM = {
-        64: 0,   # First max pooling features
-        192: 1,  # Second max pooling featurs
-        768: 2,  # Pre-aux classifier features
-        2048: 3  # Final average pooling features
-    }
-
-    def __init__(self,
-                 output_blocks=[DEFAULT_BLOCK_INDEX],
-                 resize_input=True,
-                 normalize_input=True,
-                 requires_grad=False,
-                 use_fid_inception=True):
-        """Build pretrained InceptionV3
-
-        Parameters
-        ----------
-        output_blocks : list of int
-            Indices of blocks to return features of. Possible values are:
-                - 0: corresponds to output of first max pooling
-                - 1: corresponds to output of second max pooling
-                - 2: corresponds to output which is fed to aux classifier
-                - 3: corresponds to output of final average pooling
-        resize_input : bool
-            If true, bilinearly resizes input to width and height 299 before
-            feeding input to model. As the network without fully connected
-            layers is fully convolutional, it should be able to handle inputs
-            of arbitrary size, so resizing might not be strictly needed
-        normalize_input : bool
-            If true, scales the input from range (0, 1) to the range the
-            pretrained Inception network expects, namely (-1, 1)
-        requires_grad : bool
-            If true, parameters of the model require gradients. Possibly useful
-            for finetuning the network
-        use_fid_inception : bool
-            If true, uses the pretrained Inception model used in Tensorflow's
-            FID implementation. If false, uses the pretrained Inception model
-            available in torchvision. The FID Inception model has different
-            weights and a slightly different structure from torchvision's
-            Inception model. If you want to compute FID scores, you are
-            strongly advised to set this parameter to true to get comparable
-            results.
-        """
-        super(InceptionV3, self).__init__()
-
-        self.resize_input = resize_input
-        self.normalize_input = normalize_input
-        self.output_blocks = sorted(output_blocks)
-        self.last_needed_block = max(output_blocks)
-
-        assert self.last_needed_block <= 3, \
-            'Last possible output block index is 3'
-
-        self.blocks = nn.ModuleList()
-
-        if use_fid_inception:
-            inception = fid_inception_v3()
-        else:
-            inception = models.inception_v3(pretrained=True)
-
-        # Block 0: input to maxpool1
-        block0 = [
-            inception.Conv2d_1a_3x3,
-            inception.Conv2d_2a_3x3,
-            inception.Conv2d_2b_3x3,
-            nn.MaxPool2d(kernel_size=3, stride=2)
-        ]
-        self.blocks.append(nn.Sequential(*block0))
-
-        # Block 1: maxpool1 to maxpool2
-        if self.last_needed_block >= 1:
-            block1 = [
-                inception.Conv2d_3b_1x1,
-                inception.Conv2d_4a_3x3,
-                nn.MaxPool2d(kernel_size=3, stride=2)
-            ]
-            self.blocks.append(nn.Sequential(*block1))
-
-        # Block 2: maxpool2 to aux classifier
-        if self.last_needed_block >= 2:
-            block2 = [
-                inception.Mixed_5b,
-                inception.Mixed_5c,
-                inception.Mixed_5d,
-                inception.Mixed_6a,
-                inception.Mixed_6b,
-                inception.Mixed_6c,
-                inception.Mixed_6d,
-                inception.Mixed_6e,
-            ]
-            self.blocks.append(nn.Sequential(*block2))
-
-        # Block 3: aux classifier to final avgpool
-        if self.last_needed_block >= 3:
-            block3 = [
-                inception.Mixed_7a,
-                inception.Mixed_7b,
-                inception.Mixed_7c,
-                nn.AdaptiveAvgPool2d(output_size=(1, 1))
-            ]
-            self.blocks.append(nn.Sequential(*block3))
-
-        for param in self.parameters():
-            param.requires_grad = requires_grad
-
-    def forward(self, inp):
-        """Get Inception feature maps
-
-        Parameters
-        ----------
-        inp : torch.autograd.Variable
-            Input tensor of shape Bx3xHxW. Values are expected to be in
-            range (0, 1)
-
-        Returns
-        -------
-        List of torch.autograd.Variable, corresponding to the selected output
-        block, sorted ascending by index
-        """
-        outp = []
-        x = inp
-
-        if self.resize_input:
-            x = F.interpolate(x,
-                              size=(299, 299),
-                              mode='bilinear',
-                              align_corners=False)
-
-        if self.normalize_input:
-            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)
-
-        for idx, block in enumerate(self.blocks):
-            x = block(x)
-            if idx in self.output_blocks:
-                outp.append(x)
-
-            if idx == self.last_needed_block:
-                break
-
-        return outp
-
-
-def fid_inception_v3():
-    """Build pretrained Inception model for FID computation
-
-    The Inception model for FID computation uses a different set of weights
-    and has a slightly different structure than torchvision's Inception.
-
-    This method first constructs torchvision's Inception and then patches the
-    necessary parts that are different in the FID Inception model.
-    """
-    LOGGER.info('fid_inception_v3 called')
-    inception = models.inception_v3(num_classes=1008,
-                                    aux_logits=False,
-                                    pretrained=False)
-    LOGGER.info('models.inception_v3 done')
-    inception.Mixed_5b = FIDInceptionA(192, pool_features=32)
-    inception.Mixed_5c = FIDInceptionA(256, pool_features=64)
-    inception.Mixed_5d = FIDInceptionA(288, pool_features=64)
-    inception.Mixed_6b = FIDInceptionC(768, channels_7x7=128)
-    inception.Mixed_6c = FIDInceptionC(768, channels_7x7=160)
-    inception.Mixed_6d = FIDInceptionC(768, channels_7x7=160)
-    inception.Mixed_6e = FIDInceptionC(768, channels_7x7=192)
-    inception.Mixed_7b = FIDInceptionE_1(1280)
-    inception.Mixed_7c = FIDInceptionE_2(2048)
-
-    LOGGER.info('fid_inception_v3 patching done')
-
-    state_dict = load_state_dict_from_url(FID_WEIGHTS_URL, progress=True)
-    LOGGER.info('fid_inception_v3 weights downloaded')
-
-    inception.load_state_dict(state_dict)
-    LOGGER.info('fid_inception_v3 weights loaded into model')
-
-    return inception
-
-
-class FIDInceptionA(models.inception.InceptionA):
-    """InceptionA block patched for FID computation"""
-    def __init__(self, in_channels, pool_features):
-        super(FIDInceptionA, self).__init__(in_channels, pool_features)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch5x5 = self.branch5x5_1(x)
-        branch5x5 = self.branch5x5_2(branch5x5)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionC(models.inception.InceptionC):
-    """InceptionC block patched for FID computation"""
-    def __init__(self, in_channels, channels_7x7):
-        super(FIDInceptionC, self).__init__(in_channels, channels_7x7)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch7x7 = self.branch7x7_1(x)
-        branch7x7 = self.branch7x7_2(branch7x7)
-        branch7x7 = self.branch7x7_3(branch7x7)
-
-        branch7x7dbl = self.branch7x7dbl_1(x)
-        branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
-        branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_1(models.inception.InceptionE):
-    """First InceptionE block patched for FID computation"""
-    def __init__(self, in_channels):
-        super(FIDInceptionE_1, self).__init__(in_channels)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch3x3 = self.branch3x3_1(x)
-        branch3x3 = [
-            self.branch3x3_2a(branch3x3),
-            self.branch3x3_2b(branch3x3),
-        ]
-        branch3x3 = torch.cat(branch3x3, 1)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = [
-            self.branch3x3dbl_3a(branch3x3dbl),
-            self.branch3x3dbl_3b(branch3x3dbl),
-        ]
-        branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
-        # Patch: Tensorflow's average pool does not use the padded zero's in
-        # its average calculation
-        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1,
-                                   count_include_pad=False)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)
-
-
-class FIDInceptionE_2(models.inception.InceptionE):
-    """Second InceptionE block patched for FID computation"""
-    def __init__(self, in_channels):
-        super(FIDInceptionE_2, self).__init__(in_channels)
-
-    def forward(self, x):
-        branch1x1 = self.branch1x1(x)
-
-        branch3x3 = self.branch3x3_1(x)
-        branch3x3 = [
-            self.branch3x3_2a(branch3x3),
-            self.branch3x3_2b(branch3x3),
-        ]
-        branch3x3 = torch.cat(branch3x3, 1)
-
-        branch3x3dbl = self.branch3x3dbl_1(x)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = [
-            self.branch3x3dbl_3a(branch3x3dbl),
-            self.branch3x3dbl_3b(branch3x3dbl),
-        ]
-        branch3x3dbl = torch.cat(branch3x3dbl, 1)
-
-        # Patch: The FID Inception model uses max pooling instead of average
-        # pooling. This is likely an error in this specific Inception
-        # implementation, as other Inception models use average pooling here
-        # (which matches the description in the paper).
-        branch_pool = F.max_pool2d(x, kernel_size=3, stride=1, padding=1)
-        branch_pool = self.branch_pool(branch_pool)
-
-        outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
-        return torch.cat(outputs, 1)

spaces/AlexWang/lama/saicinpainting/training/visualizers/directory.py

@@ -1,36 +0,0 @@
-import os
-
-import cv2
-import numpy as np
-
-from saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch
-from saicinpainting.utils import check_and_warn_input_range
-
-
-class DirectoryVisualizer(BaseVisualizer):
-    DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ')
-
-    def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10,
-                 last_without_mask=True, rescale_keys=None):
-        self.outdir = outdir
-        os.makedirs(self.outdir, exist_ok=True)
-        self.key_order = key_order
-        self.max_items_in_batch = max_items_in_batch
-        self.last_without_mask = last_without_mask
-        self.rescale_keys = rescale_keys
-
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image')
-        vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch,
-                                                  last_without_mask=self.last_without_mask,
-                                                  rescale_keys=self.rescale_keys)
-
-        vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')
-
-        curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}')
-        os.makedirs(curoutdir, exist_ok=True)
-        rank_suffix = f'_r{rank}' if rank is not None else ''
-        out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg')
-
-        vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)
-        cv2.imwrite(out_fname, vis_img)

spaces/Aloento/9Nine-PITS/text/frontend/normalizer/numbers.py

@@ -1,86 +0,0 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# number expansion is not that easy
-import re
-
-import inflect
-
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-
-def _remove_commas(m):
-  return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-  return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-  match = m.group(1)
-  parts = match.split('.')
-  if len(parts) > 2:
-    return match + ' dollars'  # Unexpected format
-  dollars = int(parts[0]) if parts[0] else 0
-  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-  if dollars and cents:
-    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-    cent_unit = 'cent' if cents == 1 else 'cents'
-    return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-  elif dollars:
-    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-    return '%s %s' % (dollars, dollar_unit)
-  elif cents:
-    cent_unit = 'cent' if cents == 1 else 'cents'
-    return '%s %s' % (cents, cent_unit)
-  else:
-    return 'zero dollars'
-
-
-def _expand_ordinal(m):
-  return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-  num = int(m.group(0))
-  if num > 1000 and num < 3000:
-    if num == 2000:
-      return 'two thousand'
-    elif num > 2000 and num < 2010:
-      return 'two thousand ' + _inflect.number_to_words(num % 100)
-    elif num % 100 == 0:
-      return _inflect.number_to_words(num // 100) + ' hundred'
-    else:
-      return _inflect.number_to_words(
-        num, andword='', zero='oh', group=2).replace(', ', ' ')
-  else:
-    return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-  """ Normalize numbers in English text.
-  """
-  text = re.sub(_comma_number_re, _remove_commas, text)
-  text = re.sub(_pounds_re, r'\1 pounds', text)
-  text = re.sub(_dollars_re, _expand_dollars, text)
-  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-  text = re.sub(_ordinal_re, _expand_ordinal, text)
-  text = re.sub(_number_re, _expand_number, text)
-  return text

spaces/Andres99/Tune-A-Video-Training-UI/app.py

@@ -1,84 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-from subprocess import getoutput
-
-import gradio as gr
-import torch
-
-from app_inference import create_inference_demo
-from app_training import create_training_demo
-from app_upload import create_upload_demo
-from inference import InferencePipeline
-from trainer import Trainer
-
-TITLE = '# [Tune-A-Video](https://tuneavideo.github.io/) UI'
-
-ORIGINAL_SPACE_ID = 'Tune-A-Video-library/Tune-A-Video-Training-UI'
-SPACE_ID = os.getenv('SPACE_ID', ORIGINAL_SPACE_ID)
-GPU_DATA = getoutput('nvidia-smi')
-SHARED_UI_WARNING = f'''## Attention - Training doesn't work in this shared UI. You can duplicate and use it with a paid private T4 GPU.
-
-<center><a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></center>
-'''
-
-if os.getenv('SYSTEM') == 'spaces' and SPACE_ID != ORIGINAL_SPACE_ID:
-    SETTINGS = f'<a href="https://huggingface.co/spaces/{SPACE_ID}/settings">Settings</a>'
-else:
-    SETTINGS = 'Settings'
-
-INVALID_GPU_WARNING = f'''## Attention - the specified GPU is invalid. Training may not work. Make sure you have selected a `T4 GPU` for this task.'''
-
-CUDA_NOT_AVAILABLE_WARNING = f'''## Attention - Running on CPU.
-<center>
-You can assign a GPU in the {SETTINGS} tab if you are running this on HF Spaces.
-You can use "T4 small/medium" to run this demo.
-</center>
-'''
-
-HF_TOKEN_NOT_SPECIFIED_WARNING = f'''The environment variable `HF_TOKEN` is not specified. Feel free to specify your Hugging Face token with write permission if you don't want to manually provide it for every run.
-<center>
-You can check and create your Hugging Face tokens <a href="https://huggingface.co/settings/tokens" target="_blank">here</a>.
-You can specify environment variables in the "Repository secrets" section of the {SETTINGS} tab.
-</center>
-'''
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-
-
-def show_warning(warning_text: str) -> gr.Blocks:
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown(warning_text)
-    return demo
-
-
-pipe = InferencePipeline(HF_TOKEN)
-trainer = Trainer(HF_TOKEN)
-
-with gr.Blocks(css='style.css') as demo:
-    if SPACE_ID == ORIGINAL_SPACE_ID:
-        show_warning(SHARED_UI_WARNING)
-    elif not torch.cuda.is_available():
-        show_warning(CUDA_NOT_AVAILABLE_WARNING)
-    elif (not 'T4' in GPU_DATA):
-        show_warning(INVALID_GPU_WARNING)
-
-    gr.Markdown(TITLE)
-    with gr.Tabs():
-        with gr.TabItem('Train'):
-            create_training_demo(trainer, pipe)
-        with gr.TabItem('Run'):
-            create_inference_demo(pipe, HF_TOKEN)
-        with gr.TabItem('Upload'):
-            gr.Markdown('''
-            - You can use this tab to upload models later if you choose not to upload models in training time or if upload in training time failed.
-            ''')
-            create_upload_demo(HF_TOKEN)
-
-    if not HF_TOKEN:
-        show_warning(HF_TOKEN_NOT_SPECIFIED_WARNING)
-
-demo.queue(max_size=1).launch(share=False)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/community/unclip_text_interpolation.py

@@ -1,573 +0,0 @@
-import inspect
-from typing import List, Optional, Tuple, Union
-
-import torch
-from torch.nn import functional as F
-from transformers import CLIPTextModelWithProjection, CLIPTokenizer
-from transformers.models.clip.modeling_clip import CLIPTextModelOutput
-
-from diffusers import (
-    DiffusionPipeline,
-    ImagePipelineOutput,
-    PriorTransformer,
-    UnCLIPScheduler,
-    UNet2DConditionModel,
-    UNet2DModel,
-)
-from diffusers.pipelines.unclip import UnCLIPTextProjModel
-from diffusers.utils import is_accelerate_available, logging, randn_tensor
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-def slerp(val, low, high):
-    """
-    Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic.
-    """
-    low_norm = low / torch.norm(low)
-    high_norm = high / torch.norm(high)
-    omega = torch.acos((low_norm * high_norm))
-    so = torch.sin(omega)
-    res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high
-    return res
-
-
-class UnCLIPTextInterpolationPipeline(DiffusionPipeline):
-
-    """
-    Pipeline for prompt-to-prompt interpolation on CLIP text embeddings and using the UnCLIP / Dall-E to decode them to images.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        text_proj ([`UnCLIPTextProjModel`]):
-            Utility class to prepare and combine the embeddings before they are passed to the decoder.
-        decoder ([`UNet2DConditionModel`]):
-            The decoder to invert the image embedding into an image.
-        super_res_first ([`UNet2DModel`]):
-            Super resolution unet. Used in all but the last step of the super resolution diffusion process.
-        super_res_last ([`UNet2DModel`]):
-            Super resolution unet. Used in the last step of the super resolution diffusion process.
-        prior_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the prior denoising process. Just a modified DDPMScheduler.
-        decoder_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the decoder denoising process. Just a modified DDPMScheduler.
-        super_res_scheduler ([`UnCLIPScheduler`]):
-            Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler.
-
-    """
-
-    prior: PriorTransformer
-    decoder: UNet2DConditionModel
-    text_proj: UnCLIPTextProjModel
-    text_encoder: CLIPTextModelWithProjection
-    tokenizer: CLIPTokenizer
-    super_res_first: UNet2DModel
-    super_res_last: UNet2DModel
-
-    prior_scheduler: UnCLIPScheduler
-    decoder_scheduler: UnCLIPScheduler
-    super_res_scheduler: UnCLIPScheduler
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.__init__
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        decoder: UNet2DConditionModel,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        text_proj: UnCLIPTextProjModel,
-        super_res_first: UNet2DModel,
-        super_res_last: UNet2DModel,
-        prior_scheduler: UnCLIPScheduler,
-        decoder_scheduler: UnCLIPScheduler,
-        super_res_scheduler: UnCLIPScheduler,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            decoder=decoder,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            text_proj=text_proj,
-            super_res_first=super_res_first,
-            super_res_last=super_res_last,
-            prior_scheduler=prior_scheduler,
-            decoder_scheduler=decoder_scheduler,
-            super_res_scheduler=super_res_scheduler,
-        )
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        text_model_output: Optional[Union[CLIPTextModelOutput, Tuple]] = None,
-        text_attention_mask: Optional[torch.Tensor] = None,
-    ):
-        if text_model_output is None:
-            batch_size = len(prompt) if isinstance(prompt, list) else 1
-            # get prompt text embeddings
-            text_inputs = self.tokenizer(
-                prompt,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            text_mask = text_inputs.attention_mask.bool().to(device)
-
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-                text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-
-            text_encoder_output = self.text_encoder(text_input_ids.to(device))
-
-            prompt_embeds = text_encoder_output.text_embeds
-            text_encoder_hidden_states = text_encoder_output.last_hidden_state
-
-        else:
-            batch_size = text_model_output[0].shape[0]
-            prompt_embeds, text_encoder_hidden_states = text_model_output[0], text_model_output[1]
-            text_mask = text_attention_mask
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens = [""] * batch_size
-
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
-            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
-
-            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return prompt_embeds, text_encoder_hidden_states, text_mask
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.enable_sequential_cpu_offload
-    def enable_sequential_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
-        models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
-        when their specific submodule has its `forward` method called.
-        """
-        if is_accelerate_available():
-            from accelerate import cpu_offload
-        else:
-            raise ImportError("Please install accelerate via `pip install accelerate`")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        # TODO: self.prior.post_process_latents is not covered by the offload hooks, so it fails if added to the list
-        models = [
-            self.decoder,
-            self.text_proj,
-            self.text_encoder,
-            self.super_res_first,
-            self.super_res_last,
-        ]
-        for cpu_offloaded_model in models:
-            if cpu_offloaded_model is not None:
-                cpu_offload(cpu_offloaded_model, device)
-
-    @property
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device
-    def _execution_device(self):
-        r"""
-        Returns the device on which the pipeline's models will be executed. After calling
-        `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
-        hooks.
-        """
-        if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"):
-            return self.device
-        for module in self.decoder.modules():
-            if (
-                hasattr(module, "_hf_hook")
-                and hasattr(module._hf_hook, "execution_device")
-                and module._hf_hook.execution_device is not None
-            ):
-                return torch.device(module._hf_hook.execution_device)
-        return self.device
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        start_prompt: str,
-        end_prompt: str,
-        steps: int = 5,
-        prior_num_inference_steps: int = 25,
-        decoder_num_inference_steps: int = 25,
-        super_res_num_inference_steps: int = 7,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        prior_guidance_scale: float = 4.0,
-        decoder_guidance_scale: float = 8.0,
-        enable_sequential_cpu_offload=True,
-        gpu_id=0,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            start_prompt (`str`):
-                The prompt to start the image generation interpolation from.
-            end_prompt (`str`):
-                The prompt to end the image generation interpolation at.
-            steps (`int`, *optional*, defaults to 5):
-                The number of steps over which to interpolate from start_prompt to end_prompt. The pipeline returns
-                the same number of images as this value.
-            prior_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the prior. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            decoder_num_inference_steps (`int`, *optional*, defaults to 25):
-                The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality
-                image at the expense of slower inference.
-            super_res_num_inference_steps (`int`, *optional*, defaults to 7):
-                The number of denoising steps for super resolution. More denoising steps usually lead to a higher
-                quality image at the expense of slower inference.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            prior_guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            decoder_guidance_scale (`float`, *optional*, defaults to 4.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generated image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            enable_sequential_cpu_offload (`bool`, *optional*, defaults to `True`):
-                If True, offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's
-                models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only
-                when their specific submodule has its `forward` method called.
-            gpu_id (`int`, *optional*, defaults to `0`):
-                The gpu_id to be passed to enable_sequential_cpu_offload. Only works when enable_sequential_cpu_offload is set to True.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-        """
-
-        if not isinstance(start_prompt, str) or not isinstance(end_prompt, str):
-            raise ValueError(
-                f"`start_prompt` and `end_prompt` should be of type `str` but got {type(start_prompt)} and"
-                f" {type(end_prompt)} instead"
-            )
-
-        if enable_sequential_cpu_offload:
-            self.enable_sequential_cpu_offload(gpu_id=gpu_id)
-
-        device = self._execution_device
-
-        # Turn the prompts into embeddings.
-        inputs = self.tokenizer(
-            [start_prompt, end_prompt],
-            padding="max_length",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        inputs.to(device)
-        text_model_output = self.text_encoder(**inputs)
-
-        text_attention_mask = torch.max(inputs.attention_mask[0], inputs.attention_mask[1])
-        text_attention_mask = torch.cat([text_attention_mask.unsqueeze(0)] * steps).to(device)
-
-        # Interpolate from the start to end prompt using slerp and add the generated images to an image output pipeline
-        batch_text_embeds = []
-        batch_last_hidden_state = []
-
-        for interp_val in torch.linspace(0, 1, steps):
-            text_embeds = slerp(interp_val, text_model_output.text_embeds[0], text_model_output.text_embeds[1])
-            last_hidden_state = slerp(
-                interp_val, text_model_output.last_hidden_state[0], text_model_output.last_hidden_state[1]
-            )
-            batch_text_embeds.append(text_embeds.unsqueeze(0))
-            batch_last_hidden_state.append(last_hidden_state.unsqueeze(0))
-
-        batch_text_embeds = torch.cat(batch_text_embeds)
-        batch_last_hidden_state = torch.cat(batch_last_hidden_state)
-
-        text_model_output = CLIPTextModelOutput(
-            text_embeds=batch_text_embeds, last_hidden_state=batch_last_hidden_state
-        )
-
-        batch_size = text_model_output[0].shape[0]
-
-        do_classifier_free_guidance = prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
-
-        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
-            prompt=None,
-            device=device,
-            num_images_per_prompt=1,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            text_model_output=text_model_output,
-            text_attention_mask=text_attention_mask,
-        )
-
-        # prior
-
-        self.prior_scheduler.set_timesteps(prior_num_inference_steps, device=device)
-        prior_timesteps_tensor = self.prior_scheduler.timesteps
-
-        embedding_dim = self.prior.config.embedding_dim
-
-        prior_latents = self.prepare_latents(
-            (batch_size, embedding_dim),
-            prompt_embeds.dtype,
-            device,
-            generator,
-            None,
-            self.prior_scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
-
-            predicted_image_embedding = self.prior(
-                latent_model_input,
-                timestep=t,
-                proj_embedding=prompt_embeds,
-                encoder_hidden_states=text_encoder_hidden_states,
-                attention_mask=text_mask,
-            ).predicted_image_embedding
-
-            if do_classifier_free_guidance:
-                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
-                predicted_image_embedding = predicted_image_embedding_uncond + prior_guidance_scale * (
-                    predicted_image_embedding_text - predicted_image_embedding_uncond
-                )
-
-            if i + 1 == prior_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = prior_timesteps_tensor[i + 1]
-
-            prior_latents = self.prior_scheduler.step(
-                predicted_image_embedding,
-                timestep=t,
-                sample=prior_latents,
-                generator=generator,
-                prev_timestep=prev_timestep,
-            ).prev_sample
-
-        prior_latents = self.prior.post_process_latents(prior_latents)
-
-        image_embeddings = prior_latents
-
-        # done prior
-
-        # decoder
-
-        text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj(
-            image_embeddings=image_embeddings,
-            prompt_embeds=prompt_embeds,
-            text_encoder_hidden_states=text_encoder_hidden_states,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-        )
-
-        if device.type == "mps":
-            # HACK: MPS: There is a panic when padding bool tensors,
-            # so cast to int tensor for the pad and back to bool afterwards
-            text_mask = text_mask.type(torch.int)
-            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1)
-            decoder_text_mask = decoder_text_mask.type(torch.bool)
-        else:
-            decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True)
-
-        self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device)
-        decoder_timesteps_tensor = self.decoder_scheduler.timesteps
-
-        num_channels_latents = self.decoder.config.in_channels
-        height = self.decoder.config.sample_size
-        width = self.decoder.config.sample_size
-
-        decoder_latents = self.prepare_latents(
-            (batch_size, num_channels_latents, height, width),
-            text_encoder_hidden_states.dtype,
-            device,
-            generator,
-            None,
-            self.decoder_scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents
-
-            noise_pred = self.decoder(
-                sample=latent_model_input,
-                timestep=t,
-                encoder_hidden_states=text_encoder_hidden_states,
-                class_labels=additive_clip_time_embeddings,
-                attention_mask=decoder_text_mask,
-            ).sample
-
-            if do_classifier_free_guidance:
-                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1)
-                noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1)
-                noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond)
-                noise_pred = torch.cat([noise_pred, predicted_variance], dim=1)
-
-            if i + 1 == decoder_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = decoder_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            decoder_latents = self.decoder_scheduler.step(
-                noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        decoder_latents = decoder_latents.clamp(-1, 1)
-
-        image_small = decoder_latents
-
-        # done decoder
-
-        # super res
-
-        self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device)
-        super_res_timesteps_tensor = self.super_res_scheduler.timesteps
-
-        channels = self.super_res_first.config.in_channels // 2
-        height = self.super_res_first.config.sample_size
-        width = self.super_res_first.config.sample_size
-
-        super_res_latents = self.prepare_latents(
-            (batch_size, channels, height, width),
-            image_small.dtype,
-            device,
-            generator,
-            None,
-            self.super_res_scheduler,
-        )
-
-        if device.type == "mps":
-            # MPS does not support many interpolations
-            image_upscaled = F.interpolate(image_small, size=[height, width])
-        else:
-            interpolate_antialias = {}
-            if "antialias" in inspect.signature(F.interpolate).parameters:
-                interpolate_antialias["antialias"] = True
-
-            image_upscaled = F.interpolate(
-                image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
-            )
-
-        for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)):
-            # no classifier free guidance
-
-            if i == super_res_timesteps_tensor.shape[0] - 1:
-                unet = self.super_res_last
-            else:
-                unet = self.super_res_first
-
-            latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1)
-
-            noise_pred = unet(
-                sample=latent_model_input,
-                timestep=t,
-            ).sample
-
-            if i + 1 == super_res_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = super_res_timesteps_tensor[i + 1]
-
-            # compute the previous noisy sample x_t -> x_t-1
-            super_res_latents = self.super_res_scheduler.step(
-                noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator
-            ).prev_sample
-
-        image = super_res_latents
-        # done super res
-
-        # post processing
-
-        image = image * 0.5 + 0.5
-        image = image.clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-
-        if output_type == "pil":
-            image = self.numpy_to_pil(image)
-
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)

spaces/Andy1621/uniformer_image_detection/mmdet/models/detectors/detr.py

@@ -1,46 +0,0 @@
-from mmdet.core import bbox2result
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class DETR(SingleStageDetector):
-    r"""Implementation of `DETR: End-to-End Object Detection with
-    Transformers <https://arxiv.org/pdf/2005.12872>`_"""
-
-    def __init__(self,
-                 backbone,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(DETR, self).__init__(backbone, None, bbox_head, train_cfg,
-                                   test_cfg, pretrained)
-
-    def simple_test(self, img, img_metas, rescale=False):
-        """Test function without test time augmentation.
-
-        Args:
-            imgs (list[torch.Tensor]): List of multiple images
-            img_metas (list[dict]): List of image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[list[np.ndarray]]: BBox results of each image and classes.
-                The outer list corresponds to each image. The inner list
-                corresponds to each class.
-        """
-        batch_size = len(img_metas)
-        assert batch_size == 1, 'Currently only batch_size 1 for inference ' \
-            f'mode is supported. Found batch_size {batch_size}.'
-        x = self.extract_feat(img)
-        outs = self.bbox_head(x, img_metas)
-        bbox_list = self.bbox_head.get_bboxes(
-            *outs, img_metas, rescale=rescale)
-
-        bbox_results = [
-            bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
-            for det_bboxes, det_labels in bbox_list
-        ]
-        return bbox_results

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r18-d8_512x1024_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
-    pretrained='open-mmlab://resnet18_v1c',
-    backbone=dict(depth=18),
-    decode_head=dict(
-        in_channels=512,
-        channels=128,
-    ),
-    auxiliary_head=dict(in_channels=256, channels=64))

spaces/Andy1621/uniformerv2_demo/uniformerv2.py

@@ -1,510 +0,0 @@
-#!/usr/bin/env python
-import os
-from collections import OrderedDict
-
-from timm.models.layers import DropPath
-import torch
-from torch import nn
-from torch.nn import MultiheadAttention
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-
-MODEL_PATH = './'
-_MODELS = {
-    "ViT-B/16": os.path.join(MODEL_PATH, "vit_b16.pth"),
-    "ViT-L/14": os.path.join(MODEL_PATH, "vit_l14.pth"),
-    "ViT-L/14_336": os.path.join(MODEL_PATH, "vit_l14_336.pth"),
-}
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class Local_MHRA(nn.Module):
-    def __init__(self, d_model, dw_reduction=1.5, pos_kernel_size=3):
-        super().__init__() 
-
-        padding = pos_kernel_size // 2
-        re_d_model = int(d_model // dw_reduction)
-        self.pos_embed = nn.Sequential(
-            nn.BatchNorm3d(d_model),
-            nn.Conv3d(d_model, re_d_model, kernel_size=1, stride=1, padding=0),
-            nn.Conv3d(re_d_model, re_d_model, kernel_size=(pos_kernel_size, 1, 1), stride=(1, 1, 1), padding=(padding, 0, 0), groups=re_d_model),
-            nn.Conv3d(re_d_model, d_model, kernel_size=1, stride=1, padding=0),
-        )
-
-        # init zero
-        print('Init zero for Conv in pos_emb')
-        nn.init.constant_(self.pos_embed[3].weight, 0)
-        nn.init.constant_(self.pos_embed[3].bias, 0)
-
-    def forward(self, x):
-        return self.pos_embed(x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(
-            self, d_model, n_head, attn_mask=None, drop_path=0.0, 
-            dw_reduction=1.5, no_lmhra=False, double_lmhra=True
-        ):
-        super().__init__() 
-        
-        self.n_head = n_head
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        print(f'Drop path rate: {drop_path}')
-
-        self.no_lmhra = no_lmhra
-        self.double_lmhra = double_lmhra
-        print(f'No L_MHRA: {no_lmhra}')
-        print(f'Double L_MHRA: {double_lmhra}')
-        if not no_lmhra:
-            self.lmhra1 = Local_MHRA(d_model, dw_reduction=dw_reduction)
-            if double_lmhra:
-                self.lmhra2 = Local_MHRA(d_model, dw_reduction=dw_reduction)
-
-        # spatial
-        self.attn = MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x, T=8, use_checkpoint=False):
-        # x: 1+HW, NT, C
-        if not self.no_lmhra:
-            # Local MHRA
-            tmp_x = x[1:, :, :]
-            L, NT, C = tmp_x.shape
-            N = NT // T
-            H = W = int(L ** 0.5)
-            tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous()
-            tmp_x = tmp_x + self.drop_path(self.lmhra1(tmp_x))
-            tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C)
-            x = torch.cat([x[:1, :, :], tmp_x], dim=0)
-        # MHSA
-        if use_checkpoint:
-            attn_out = checkpoint.checkpoint(self.attention, self.ln_1(x))
-            x = x + self.drop_path(attn_out)
-        else:
-            x = x + self.drop_path(self.attention(self.ln_1(x)))
-        # Local MHRA
-        if not self.no_lmhra and self.double_lmhra:
-            tmp_x = x[1:, :, :]
-            tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0, 1).contiguous()
-            tmp_x = tmp_x + self.drop_path(self.lmhra2(tmp_x))
-            tmp_x = tmp_x.view(N, C, T, L).permute(3, 0, 2, 1).contiguous().view(L, NT, C)
-            x = torch.cat([x[:1, :, :], tmp_x], dim=0)
-        # FFN
-        if use_checkpoint:
-            mlp_out = checkpoint.checkpoint(self.mlp, self.ln_2(x))
-            x = x + self.drop_path(mlp_out)
-        else:
-            x = x + self.drop_path(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Extractor(nn.Module):
-    def __init__(
-            self, d_model, n_head, attn_mask=None,
-            mlp_factor=4.0, dropout=0.0, drop_path=0.0,
-        ):
-        super().__init__()
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        print(f'Drop path rate: {drop_path}')
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = nn.LayerNorm(d_model)
-        d_mlp = round(mlp_factor * d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_mlp)),
-            ("gelu", QuickGELU()),
-            ("dropout", nn.Dropout(dropout)),
-            ("c_proj", nn.Linear(d_mlp, d_model))
-        ]))
-        self.ln_2 = nn.LayerNorm(d_model)
-        self.ln_3 = nn.LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-        # zero init
-        nn.init.xavier_uniform_(self.attn.in_proj_weight)
-        nn.init.constant_(self.attn.out_proj.weight, 0.)
-        nn.init.constant_(self.attn.out_proj.bias, 0.)
-        nn.init.xavier_uniform_(self.mlp[0].weight)
-        nn.init.constant_(self.mlp[-1].weight, 0.)
-        nn.init.constant_(self.mlp[-1].bias, 0.)
-
-    def attention(self, x, y):
-        d_model = self.ln_1.weight.size(0)
-        q = (x @ self.attn.in_proj_weight[:d_model].T) + self.attn.in_proj_bias[:d_model]
-
-        k = (y @ self.attn.in_proj_weight[d_model:-d_model].T) + self.attn.in_proj_bias[d_model:-d_model]
-        v = (y @ self.attn.in_proj_weight[-d_model:].T) + self.attn.in_proj_bias[-d_model:]
-        Tx, Ty, N = q.size(0), k.size(0), q.size(1)
-        q = q.view(Tx, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
-        k = k.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
-        v = v.view(Ty, N, self.attn.num_heads, self.attn.head_dim).permute(1, 2, 0, 3)
-        aff = (q @ k.transpose(-2, -1) / (self.attn.head_dim ** 0.5))
-
-        aff = aff.softmax(dim=-1)
-        out = aff @ v
-        out = out.permute(2, 0, 1, 3).flatten(2)
-        out = self.attn.out_proj(out)
-        return out
-
-    def forward(self, x, y):
-        x = x + self.drop_path(self.attention(self.ln_1(x), self.ln_3(y)))
-        x = x + self.drop_path(self.mlp(self.ln_2(x)))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(
-            self, width, layers, heads, attn_mask=None, backbone_drop_path_rate=0., 
-            use_checkpoint=False, checkpoint_num=[0], t_size=8, dw_reduction=2,
-            no_lmhra=False, double_lmhra=True,
-            return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
-            n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
-            mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 
-            cls_dropout=0.5, num_classes=400,
-        ):
-        super().__init__()
-        self.T = t_size
-        self.return_list = return_list
-        # backbone
-        b_dpr = [x.item() for x in torch.linspace(0, backbone_drop_path_rate, layers)]
-        self.resblocks = nn.ModuleList([
-            ResidualAttentionBlock(
-                width, heads, attn_mask, 
-                drop_path=b_dpr[i],
-                dw_reduction=dw_reduction,
-                no_lmhra=no_lmhra,
-                double_lmhra=double_lmhra,
-            ) for i in range(layers)
-        ])
-        # checkpoint
-        self.use_checkpoint = use_checkpoint
-        self.checkpoint_num = checkpoint_num
-        self.n_layers = n_layers
-        print(f'Use checkpoint: {self.use_checkpoint}')
-        print(f'Checkpoint number: {self.checkpoint_num}')
-
-        # global block
-        assert n_layers == len(return_list)
-        if n_layers > 0:
-            self.temporal_cls_token = nn.Parameter(torch.zeros(1, 1, n_dim))
-            self.dpe = nn.ModuleList([
-                nn.Conv3d(n_dim, n_dim, kernel_size=3, stride=1, padding=1, bias=True, groups=n_dim)
-                for i in range(n_layers)
-            ])
-            for m in self.dpe:
-                nn.init.constant_(m.bias, 0.)
-            dpr = [x.item() for x in torch.linspace(0, drop_path_rate, n_layers)]
-            self.dec = nn.ModuleList([
-                Extractor(
-                    n_dim, n_head, mlp_factor=mlp_factor, 
-                    dropout=mlp_dropout[i], drop_path=dpr[i],
-                ) for i in range(n_layers)
-            ])
-            self.balance = nn.Parameter(torch.zeros((n_dim)))
-            self.sigmoid = nn.Sigmoid()
-        # projection
-        self.proj = nn.Sequential(
-            nn.LayerNorm(n_dim),
-            nn.Dropout(cls_dropout),
-            nn.Linear(n_dim, num_classes),
-        )
-
-    def forward(self, x):
-        T_down = self.T
-        L, NT, C = x.shape
-        N = NT // T_down
-        H = W = int((L - 1) ** 0.5)
-
-        if self.n_layers > 0:
-            cls_token = self.temporal_cls_token.repeat(1, N, 1)
-
-        j = -1
-        for i, resblock in enumerate(self.resblocks):
-            if self.use_checkpoint and i < self.checkpoint_num[0]:
-                x = resblock(x, self.T, use_checkpoint=True)
-            else:
-                x = resblock(x, T_down)
-            if i in self.return_list:
-                j += 1
-                tmp_x = x.clone()
-                tmp_x = tmp_x.view(L, N, T_down, C)
-                # dpe
-                _, tmp_feats = tmp_x[:1], tmp_x[1:]
-                tmp_feats = tmp_feats.permute(1, 3, 2, 0).reshape(N, C, T_down, H, W)
-                tmp_feats = self.dpe[j](tmp_feats).view(N, C, T_down, L - 1).permute(3, 0, 2, 1).contiguous()
-                tmp_x[1:] = tmp_x[1:] + tmp_feats
-                # global block
-                tmp_x = tmp_x.permute(2, 0, 1, 3).flatten(0, 1)  # T * L, N, C
-                cls_token = self.dec[j](cls_token, tmp_x)
-
-        if self.n_layers > 0:
-            weight = self.sigmoid(self.balance)
-            residual = x.view(L, N, T_down, C)[0].mean(1)  # L, N, T, C
-            return self.proj((1 - weight) * cls_token[0, :, :] + weight * residual)
-        else:
-            residual = x.view(L, N, T_down, C)[0].mean(1)  # L, N, T, C
-            return self.proj(residual)
-
-
-class VisionTransformer(nn.Module):
-    def __init__(
-        self, 
-        # backbone
-        input_resolution, patch_size, width, layers, heads, output_dim, backbone_drop_path_rate=0.,
-        use_checkpoint=False, checkpoint_num=[0], t_size=8, kernel_size=3, dw_reduction=1.5,
-        temporal_downsample=True,
-        no_lmhra=-False, double_lmhra=True,
-        # global block
-        return_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
-        n_layers=12, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
-        mlp_dropout=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], 
-        cls_dropout=0.5, num_classes=400,
-    ):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        padding = (kernel_size - 1) // 2
-        if temporal_downsample:
-            self.conv1 = nn.Conv3d(3, width, (kernel_size, patch_size, patch_size), (2, patch_size, patch_size), (padding, 0, 0), bias=False)
-            t_size = t_size // 2
-        else:
-            self.conv1 = nn.Conv3d(3, width, (1, patch_size, patch_size), (1, patch_size, patch_size), (0, 0, 0), bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-        
-        self.transformer = Transformer(
-            width, layers, heads, dw_reduction=dw_reduction, 
-            backbone_drop_path_rate=backbone_drop_path_rate, 
-            use_checkpoint=use_checkpoint, checkpoint_num=checkpoint_num, t_size=t_size,
-            no_lmhra=no_lmhra, double_lmhra=double_lmhra,
-            return_list=return_list, n_layers=n_layers, n_dim=n_dim, n_head=n_head, 
-            mlp_factor=mlp_factor, drop_path_rate=drop_path_rate, mlp_dropout=mlp_dropout, 
-            cls_dropout=cls_dropout, num_classes=num_classes,
-        )
-
-    def forward(self, x):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        N, C, T, H, W = x.shape
-        x = x.permute(0, 2, 3, 4, 1).reshape(N * T, H * W, C)
-        
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        out = self.transformer(x)
-        return out
-
-
-def inflate_weight(weight_2d, time_dim, center=True):
-    print(f'Init center: {center}')
-    if center:
-        weight_3d = torch.zeros(*weight_2d.shape)
-        weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
-        middle_idx = time_dim // 2
-        weight_3d[:, :, middle_idx, :, :] = weight_2d
-    else:
-        weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
-        weight_3d = weight_3d / time_dim
-    return weight_3d
-
-
-def load_state_dict(model, state_dict):
-    state_dict_3d = model.state_dict()
-    for k in state_dict.keys():
-        if state_dict[k].shape != state_dict_3d[k].shape:
-            if len(state_dict_3d[k].shape) <= 2:
-                print(f'Ignore: {k}')
-                continue
-            print(f'Inflate: {k}, {state_dict[k].shape} => {state_dict_3d[k].shape}')
-            time_dim = state_dict_3d[k].shape[2]
-            state_dict[k] = inflate_weight(state_dict[k], time_dim)
-    model.load_state_dict(state_dict, strict=False)
-
-
-def uniformerv2_b16(
-    pretrained=True, use_checkpoint=False, checkpoint_num=[0],
-    t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., 
-    temporal_downsample=True,
-    no_lmhra=False, double_lmhra=True,
-    return_list=[8, 9, 10, 11], 
-    n_layers=4, n_dim=768, n_head=12, mlp_factor=4.0, drop_path_rate=0.,
-    mlp_dropout=[0.5, 0.5, 0.5, 0.5], 
-    cls_dropout=0.5, num_classes=400,
-):
-    model = VisionTransformer(
-        input_resolution=224,
-        patch_size=16,
-        width=768,
-        layers=12,
-        heads=12,
-        output_dim=512,
-        use_checkpoint=use_checkpoint,
-        checkpoint_num=checkpoint_num,
-        t_size=t_size,
-        dw_reduction=dw_reduction, 
-        backbone_drop_path_rate=backbone_drop_path_rate, 
-        temporal_downsample=temporal_downsample,
-        no_lmhra=no_lmhra,
-        double_lmhra=double_lmhra,
-        return_list=return_list, 
-        n_layers=n_layers, 
-        n_dim=n_dim, 
-        n_head=n_head, 
-        mlp_factor=mlp_factor, 
-        drop_path_rate=drop_path_rate, 
-        mlp_dropout=mlp_dropout, 
-        cls_dropout=cls_dropout, 
-        num_classes=num_classes,
-    )
-
-    if pretrained:
-        print('load pretrained weights')
-        state_dict = torch.load(_MODELS["ViT-B/16"], map_location='cpu')
-        load_state_dict(model, state_dict)
-    return model.eval()
-
-
-def uniformerv2_l14(
-    pretrained=True, use_checkpoint=False, checkpoint_num=[0],
-    t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., 
-    temporal_downsample=True,
-    no_lmhra=False, double_lmhra=True,
-    return_list=[20, 21, 22, 23],
-    n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0.,
-    mlp_dropout=[0.5, 0.5, 0.5, 0.5], 
-    cls_dropout=0.5, num_classes=400,
-):
-    model = VisionTransformer(
-        input_resolution=224,
-        patch_size=14,
-        width=1024,
-        layers=24,
-        heads=16,
-        output_dim=768,
-        use_checkpoint=use_checkpoint,
-        checkpoint_num=checkpoint_num,
-        t_size=t_size,
-        dw_reduction=dw_reduction, 
-        backbone_drop_path_rate=backbone_drop_path_rate, 
-        temporal_downsample=temporal_downsample,
-        no_lmhra=no_lmhra,
-        double_lmhra=double_lmhra,
-        return_list=return_list, 
-        n_layers=n_layers, 
-        n_dim=n_dim, 
-        n_head=n_head, 
-        mlp_factor=mlp_factor, 
-        drop_path_rate=drop_path_rate, 
-        mlp_dropout=mlp_dropout, 
-        cls_dropout=cls_dropout, 
-        num_classes=num_classes,
-    )
-
-    if pretrained:
-        print('load pretrained weights')
-        state_dict = torch.load(_MODELS["ViT-L/14"], map_location='cpu')
-        load_state_dict(model, state_dict)
-    return model.eval()
-
-
-def uniformerv2_l14_336(
-    pretrained=True, use_checkpoint=False, checkpoint_num=[0],
-    t_size=16, dw_reduction=1.5, backbone_drop_path_rate=0., 
-    no_temporal_downsample=True,
-    no_lmhra=False, double_lmhra=True,
-    return_list=[20, 21, 22, 23],
-    n_layers=4, n_dim=1024, n_head=16, mlp_factor=4.0, drop_path_rate=0.,
-    mlp_dropout=[0.5, 0.5, 0.5, 0.5], 
-    cls_dropout=0.5, num_classes=400,
-):
-    model = VisionTransformer(
-        input_resolution=336,
-        patch_size=14,
-        width=1024,
-        layers=24,
-        heads=16,
-        output_dim=768,
-        use_checkpoint=use_checkpoint,
-        checkpoint_num=checkpoint_num,
-        t_size=t_size,
-        dw_reduction=dw_reduction, 
-        backbone_drop_path_rate=backbone_drop_path_rate, 
-        no_temporal_downsample=no_temporal_downsample,
-        no_lmhra=no_lmhra,
-        double_lmhra=double_lmhra,
-        return_list=return_list, 
-        n_layers=n_layers, 
-        n_dim=n_dim, 
-        n_head=n_head, 
-        mlp_factor=mlp_factor, 
-        drop_path_rate=drop_path_rate, 
-        mlp_dropout=mlp_dropout, 
-        cls_dropout=cls_dropout, 
-        num_classes=num_classes,
-    )
-
-    if pretrained:
-        print('load pretrained weights')
-        state_dict = torch.load(_MODELS["ViT-L/14_336"], map_location='cpu')
-        load_state_dict(model, state_dict)
-    return model.eval()
-
-
-if __name__ == '__main__':
-    import time
-    from fvcore.nn import FlopCountAnalysis
-    from fvcore.nn import flop_count_table
-    import numpy as np
-
-    seed = 4217
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    num_frames = 16
-
-    model = uniformerv2_l14(
-        pretrained=False, 
-        t_size=num_frames, backbone_drop_path_rate=0., drop_path_rate=0.,
-        dw_reduction=1.5,
-        no_lmhra=False,
-        temporal_downsample=True,
-        return_list=[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
-        mlp_dropout=[0.5]*16,
-        n_layers=16
-    )
-    print(model)
-
-    flops = FlopCountAnalysis(model, torch.rand(1, 3, num_frames, 224, 224))
-    s = time.time()
-    print(flop_count_table(flops, max_depth=1))
-    print(time.time()-s)

spaces/AntiUser/DeepDanbooru_string/app.py

@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import html
-import pathlib
-import tarfile
-
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-import piexif
-import piexif.helper
-
-TITLE = 'DeepDanbooru String'
-
-TOKEN = os.environ['TOKEN']
-MODEL_REPO = 'CikeyQI/DeepDanbooru_string'
-MODEL_FILENAME = 'model-resnet_custom_v3.h5'
-LABEL_FILENAME = 'tags.txt'
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--score-slider-step', type=float, default=0.05)
-    parser.add_argument('--score-threshold', type=float, default=0.5)
-    parser.add_argument('--theme', type=str, default='dark-grass')
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
-
-
-def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
-    if not image_dir.exists():
-        dataset_repo = 'hysts/sample-images-TADNE'
-        path = huggingface_hub.hf_hub_download(dataset_repo,
-                                               'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=TOKEN)
-        with tarfile.open(path) as f:
-            f.extractall()
-    return sorted(image_dir.glob('*'))
-
-
-def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           MODEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    model = tf.keras.models.load_model(path)
-    return model
-
-
-def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           LABEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    with open(path) as f:
-        labels = [line.strip() for line in f.readlines()]
-    return labels
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def predict(image: PIL.Image.Image, score_threshold: float,
-            model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
-    rawimage = image
-    _, height, width, _ = model.input_shape
-    image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
-    image = image.numpy()
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
-    probs = model.predict(image[None, ...])[0]
-    probs = probs.astype(float)
-    res = dict()
-    for prob, label in zip(probs.tolist(), labels):
-        if prob < score_threshold:
-            continue
-        res[label] = prob
-    b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
-    a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
-    c = ', '.join(list(b.keys()))
-    
-    items = rawimage.info
-    geninfo = ''
-    
-    if "exif" in rawimage.info:
-        exif = piexif.load(rawimage.info["exif"])
-        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
-        try:
-            exif_comment = piexif.helper.UserComment.load(exif_comment)
-        except ValueError:
-            exif_comment = exif_comment.decode('utf8', errors="ignore")
-    
-        items['exif comment'] = exif_comment
-        geninfo = exif_comment
-    
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
-    
-    geninfo = items.get('parameters', geninfo)
-    
-    info = f"""
-<p><h4>PNG Info</h4></p>    
-"""
-    for key, text in items.items():
-        info += f"""
-<div>
-<p><b>{plaintext_to_html(str(key))}</b></p>
-<p>{plaintext_to_html(str(text))}</p>
-</div>
-""".strip()+"\n"
-    
-    if len(info) == 0:
-        message = "Nothing found in the image."
-        info = f"<div><p>{message}<p></div>"
-    
-    return (a,c,res,info)
-
-
-def main():
-    args = parse_args()
-    model = load_model()
-    labels = load_labels()
-
-    func = functools.partial(predict, model=model, labels=labels)
-    func = functools.update_wrapper(func, predict)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='pil', label='Input'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=args.score_slider_step,
-                             default=args.score_threshold,
-                             label='Score Threshold'),
-        ],
-        [
-            gr.outputs.Textbox(label='Output (string)'), 
-            gr.outputs.Textbox(label='Output (raw string)'), 
-            gr.outputs.Label(label='Output (label)'),
-            gr.outputs.HTML()
-        ],
-        examples=[
-        ['miku.jpg',0.5],
-        ['miku2.jpg',0.5]
-        ],
-        title=TITLE,
-        description='''
-Demo for [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) with "ready to copy" prompt and a prompt analyzer.
-
-Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-
-PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-        ''',
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()

spaces/Ariharasudhan/YoloV5/utils/loggers/wandb/wandb_utils.py

@@ -1,589 +0,0 @@
-"""Utilities and tools for tracking runs with Weights & Biases."""
-
-import logging
-import os
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Dict
-
-import yaml
-from tqdm import tqdm
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-from utils.dataloaders import LoadImagesAndLabels, img2label_paths
-from utils.general import LOGGER, check_dataset, check_file
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')  # verify package import not local dir
-except (ImportError, AssertionError):
-    wandb = None
-
-RANK = int(os.getenv('RANK', -1))
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
-    return from_string[len(prefix):]
-
-
-def check_wandb_config_file(data_config_file):
-    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
-    if Path(wandb_config).is_file():
-        return wandb_config
-    return data_config_file
-
-
-def check_wandb_dataset(data_file):
-    is_trainset_wandb_artifact = False
-    is_valset_wandb_artifact = False
-    if isinstance(data_file, dict):
-        # In that case another dataset manager has already processed it and we don't have to
-        return data_file
-    if check_file(data_file) and data_file.endswith('.yaml'):
-        with open(data_file, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)
-        is_trainset_wandb_artifact = isinstance(data_dict['train'],
-                                                str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX)
-        is_valset_wandb_artifact = isinstance(data_dict['val'],
-                                              str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX)
-    if is_trainset_wandb_artifact or is_valset_wandb_artifact:
-        return data_dict
-    else:
-        return check_dataset(data_file)
-
-
-def get_run_info(run_path):
-    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
-    run_id = run_path.stem
-    project = run_path.parent.stem
-    entity = run_path.parent.parent.stem
-    model_artifact_name = 'run_' + run_id + '_model'
-    return entity, project, run_id, model_artifact_name
-
-
-def check_wandb_resume(opt):
-    process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
-    if isinstance(opt.resume, str):
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            if RANK not in [-1, 0]:  # For resuming DDP runs
-                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
-                api = wandb.Api()
-                artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
-                modeldir = artifact.download()
-                opt.weights = str(Path(modeldir) / "last.pt")
-            return True
-    return None
-
-
-def process_wandb_config_ddp_mode(opt):
-    with open(check_file(opt.data), errors='ignore') as f:
-        data_dict = yaml.safe_load(f)  # data dict
-    train_dir, val_dir = None, None
-    if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
-        api = wandb.Api()
-        train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
-        train_dir = train_artifact.download()
-        train_path = Path(train_dir) / 'data/images/'
-        data_dict['train'] = str(train_path)
-
-    if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
-        api = wandb.Api()
-        val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
-        val_dir = val_artifact.download()
-        val_path = Path(val_dir) / 'data/images/'
-        data_dict['val'] = str(val_path)
-    if train_dir or val_dir:
-        ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
-        with open(ddp_data_path, 'w') as f:
-            yaml.safe_dump(data_dict, f)
-        opt.data = ddp_data_path
-
-
-class WandbLogger():
-    """Log training runs, datasets, models, and predictions to Weights & Biases.
-
-    This logger sends information to W&B at wandb.ai. By default, this information
-    includes hyperparameters, system configuration and metrics, model metrics,
-    and basic data metrics and analyses.
-
-    By providing additional command line arguments to train.py, datasets,
-    models and predictions can also be logged.
-
-    For more on how this logger is used, see the Weights & Biases documentation:
-    https://docs.wandb.com/guides/integrations/yolov5
-    """
-
-    def __init__(self, opt, run_id=None, job_type='Training'):
-        """
-        - Initialize WandbLogger instance
-        - Upload dataset if opt.upload_dataset is True
-        - Setup training processes if job_type is 'Training'
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        run_id (str) -- Run ID of W&B run to be resumed
-        job_type (str) -- To set the job_type for this run
-
-       """
-        # Temporary-fix
-        if opt.upload_dataset:
-            opt.upload_dataset = False
-            # LOGGER.info("Uploading Dataset functionality is not being supported temporarily due to a bug.")
-
-        # Pre-training routine --
-        self.job_type = job_type
-        self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
-        self.val_artifact, self.train_artifact = None, None
-        self.train_artifact_path, self.val_artifact_path = None, None
-        self.result_artifact = None
-        self.val_table, self.result_table = None, None
-        self.bbox_media_panel_images = []
-        self.val_table_path_map = None
-        self.max_imgs_to_log = 16
-        self.wandb_artifact_data_dict = None
-        self.data_dict = None
-        # It's more elegant to stick to 1 wandb.init call,
-        #  but useful config data is overwritten in the WandbLogger's wandb.init call
-        if isinstance(opt.resume, str):  # checks resume from artifact
-            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-                entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
-                model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
-                assert wandb, 'install wandb to resume wandb runs'
-                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
-                self.wandb_run = wandb.init(id=run_id,
-                                            project=project,
-                                            entity=entity,
-                                            resume='allow',
-                                            allow_val_change=True)
-                opt.resume = model_artifact_name
-        elif self.wandb:
-            self.wandb_run = wandb.init(config=opt,
-                                        resume="allow",
-                                        project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
-                                        entity=opt.entity,
-                                        name=opt.name if opt.name != 'exp' else None,
-                                        job_type=job_type,
-                                        id=run_id,
-                                        allow_val_change=True) if not wandb.run else wandb.run
-        if self.wandb_run:
-            if self.job_type == 'Training':
-                if opt.upload_dataset:
-                    if not opt.resume:
-                        self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
-
-                if isinstance(opt.data, dict):
-                    # This means another dataset manager has already processed the dataset info (e.g. ClearML)
-                    # and they will have stored the already processed dict in opt.data
-                    self.data_dict = opt.data
-                elif opt.resume:
-                    # resume from artifact
-                    if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-                        self.data_dict = dict(self.wandb_run.config.data_dict)
-                    else:  # local resume
-                        self.data_dict = check_wandb_dataset(opt.data)
-                else:
-                    self.data_dict = check_wandb_dataset(opt.data)
-                    self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
-
-                    # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
-                    self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict}, allow_val_change=True)
-                self.setup_training(opt)
-
-            if self.job_type == 'Dataset Creation':
-                self.wandb_run.config.update({"upload_dataset": True})
-                self.data_dict = self.check_and_upload_dataset(opt)
-
-    def check_and_upload_dataset(self, opt):
-        """
-        Check if the dataset format is compatible and upload it as W&B artifact
-
-        arguments:
-        opt (namespace)-- Commandline arguments for current run
-
-        returns:
-        Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
-        """
-        assert wandb, 'Install wandb to upload dataset'
-        config_path = self.log_dataset_artifact(opt.data, opt.single_cls,
-                                                'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
-        with open(config_path, errors='ignore') as f:
-            wandb_data_dict = yaml.safe_load(f)
-        return wandb_data_dict
-
-    def setup_training(self, opt):
-        """
-        Setup the necessary processes for training YOLO models:
-          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
-          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
-          - Setup log_dict, initialize bbox_interval
-
-        arguments:
-        opt (namespace) -- commandline arguments for this run
-
-        """
-        self.log_dict, self.current_epoch = {}, 0
-        self.bbox_interval = opt.bbox_interval
-        if isinstance(opt.resume, str):
-            modeldir, _ = self.download_model_artifact(opt)
-            if modeldir:
-                self.weights = Path(modeldir) / "last.pt"
-                config = self.wandb_run.config
-                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
-                    self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\
-                    config.hyp, config.imgsz
-        data_dict = self.data_dict
-        if self.val_artifact is None:  # If --upload_dataset is set, use the existing artifact, don't download
-            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(
-                data_dict.get('train'), opt.artifact_alias)
-            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(
-                data_dict.get('val'), opt.artifact_alias)
-
-        if self.train_artifact_path is not None:
-            train_path = Path(self.train_artifact_path) / 'data/images/'
-            data_dict['train'] = str(train_path)
-        if self.val_artifact_path is not None:
-            val_path = Path(self.val_artifact_path) / 'data/images/'
-            data_dict['val'] = str(val_path)
-
-        if self.val_artifact is not None:
-            self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-            columns = ["epoch", "id", "ground truth", "prediction"]
-            columns.extend(self.data_dict['names'])
-            self.result_table = wandb.Table(columns)
-            self.val_table = self.val_artifact.get("val")
-            if self.val_table_path_map is None:
-                self.map_val_table_path()
-        if opt.bbox_interval == -1:
-            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
-            if opt.evolve or opt.noplots:
-                self.bbox_interval = opt.bbox_interval = opt.epochs + 1  # disable bbox_interval
-        train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
-        # Update the the data_dict to point to local artifacts dir
-        if train_from_artifact:
-            self.data_dict = data_dict
-
-    def download_dataset_artifact(self, path, alias):
-        """
-        download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
-
-        arguments:
-        path -- path of the dataset to be used for training
-        alias (str)-- alias of the artifact to be download/used for training
-
-        returns:
-        (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
-        is found otherwise returns (None, None)
-        """
-        if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
-            artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
-            dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
-            assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
-            datadir = dataset_artifact.download()
-            return datadir, dataset_artifact
-        return None, None
-
-    def download_model_artifact(self, opt):
-        """
-        download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        """
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
-            assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
-            modeldir = model_artifact.download()
-            # epochs_trained = model_artifact.metadata.get('epochs_trained')
-            total_epochs = model_artifact.metadata.get('total_epochs')
-            is_finished = total_epochs is None
-            assert not is_finished, 'training is finished, can only resume incomplete runs.'
-            return modeldir, model_artifact
-        return None, None
-
-    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
-        """
-        Log the model checkpoint as W&B artifact
-
-        arguments:
-        path (Path)   -- Path of directory containing the checkpoints
-        opt (namespace) -- Command line arguments for this run
-        epoch (int)  -- Current epoch number
-        fitness_score (float) -- fitness score for current epoch
-        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
-        """
-        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
-                                        type='model',
-                                        metadata={
-                                            'original_url': str(path),
-                                            'epochs_trained': epoch + 1,
-                                            'save period': opt.save_period,
-                                            'project': opt.project,
-                                            'total_epochs': opt.epochs,
-                                            'fitness_score': fitness_score})
-        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
-        wandb.log_artifact(model_artifact,
-                           aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
-        LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
-
-    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
-        """
-        Log the dataset as W&B artifact and return the new data file with W&B links
-
-        arguments:
-        data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
-        single_class (boolean)  -- train multi-class data as single-class
-        project (str) -- project name. Used to construct the artifact path
-        overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
-        file with _wandb postfix. Eg -> data_wandb.yaml
-
-        returns:
-        the new .yaml file with artifact links. it can be used to start training directly from artifacts
-        """
-        upload_dataset = self.wandb_run.config.upload_dataset
-        log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'
-        self.data_dict = check_dataset(data_file)  # parse and check
-        data = dict(self.data_dict)
-        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
-        names = {k: v for k, v in enumerate(names)}  # to index dictionary
-
-        # log train set
-        if not log_val_only:
-            self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(data['train'], rect=True, batch_size=1),
-                                                            names,
-                                                            name='train') if data.get('train') else None
-            if data.get('train'):
-                data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
-
-        self.val_artifact = self.create_dataset_table(
-            LoadImagesAndLabels(data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
-        if data.get('val'):
-            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
-
-        path = Path(data_file)
-        # create a _wandb.yaml file with artifacts links if both train and test set are logged
-        if not log_val_only:
-            path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml'  # updated data.yaml path
-            path = ROOT / 'data' / path
-            data.pop('download', None)
-            data.pop('path', None)
-            with open(path, 'w') as f:
-                yaml.safe_dump(data, f)
-                LOGGER.info(f"Created dataset config file {path}")
-
-        if self.job_type == 'Training':  # builds correct artifact pipeline graph
-            if not log_val_only:
-                self.wandb_run.log_artifact(
-                    self.train_artifact)  # calling use_artifact downloads the dataset. NOT NEEDED!
-            self.wandb_run.use_artifact(self.val_artifact)
-            self.val_artifact.wait()
-            self.val_table = self.val_artifact.get('val')
-            self.map_val_table_path()
-        else:
-            self.wandb_run.log_artifact(self.train_artifact)
-            self.wandb_run.log_artifact(self.val_artifact)
-        return path
-
-    def map_val_table_path(self):
-        """
-        Map the validation dataset Table like name of file -> it's id in the W&B Table.
-        Useful for - referencing artifacts for evaluation.
-        """
-        self.val_table_path_map = {}
-        LOGGER.info("Mapping dataset")
-        for i, data in enumerate(tqdm(self.val_table.data)):
-            self.val_table_path_map[data[3]] = data[0]
-
-    def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):
-        """
-        Create and return W&B artifact containing W&B Table of the dataset.
-
-        arguments:
-        dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
-        class_to_id -- hash map that maps class ids to labels
-        name -- name of the artifact
-
-        returns:
-        dataset artifact to be logged or used
-        """
-        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
-        artifact = wandb.Artifact(name=name, type="dataset")
-        img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
-        img_files = tqdm(dataset.im_files) if not img_files else img_files
-        for img_file in img_files:
-            if Path(img_file).is_dir():
-                artifact.add_dir(img_file, name='data/images')
-                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
-                artifact.add_dir(labels_path, name='data/labels')
-            else:
-                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
-                label_file = Path(img2label_paths([img_file])[0])
-                artifact.add_file(str(label_file), name='data/labels/' +
-                                  label_file.name) if label_file.exists() else None
-        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
-        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
-        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
-            box_data, img_classes = [], {}
-            for cls, *xywh in labels[:, 1:].tolist():
-                cls = int(cls)
-                box_data.append({
-                    "position": {
-                        "middle": [xywh[0], xywh[1]],
-                        "width": xywh[2],
-                        "height": xywh[3]},
-                    "class_id": cls,
-                    "box_caption": "%s" % (class_to_id[cls])})
-                img_classes[cls] = class_to_id[cls]
-            boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
-            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
-                           Path(paths).name)
-        artifact.add(table, name)
-        return artifact
-
-    def log_training_progress(self, predn, path, names):
-        """
-        Build evaluation Table. Uses reference from validation dataset table.
-
-        arguments:
-        predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image
-        names (dict(int, str)): hash map that maps class ids to labels
-        """
-        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
-        box_data = []
-        avg_conf_per_class = [0] * len(self.data_dict['names'])
-        pred_class_count = {}
-        for *xyxy, conf, cls in predn.tolist():
-            if conf >= 0.25:
-                cls = int(cls)
-                box_data.append({
-                    "position": {
-                        "minX": xyxy[0],
-                        "minY": xyxy[1],
-                        "maxX": xyxy[2],
-                        "maxY": xyxy[3]},
-                    "class_id": cls,
-                    "box_caption": f"{names[cls]} {conf:.3f}",
-                    "scores": {
-                        "class_score": conf},
-                    "domain": "pixel"})
-                avg_conf_per_class[cls] += conf
-
-                if cls in pred_class_count:
-                    pred_class_count[cls] += 1
-                else:
-                    pred_class_count[cls] = 1
-
-        for pred_class in pred_class_count.keys():
-            avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]
-
-        boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-        id = self.val_table_path_map[Path(path).name]
-        self.result_table.add_data(self.current_epoch, id, self.val_table.data[id][1],
-                                   wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
-                                   *avg_conf_per_class)
-
-    def val_one_image(self, pred, predn, path, names, im):
-        """
-        Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
-
-        arguments:
-        pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
-        path (str): local path of the current evaluation image
-        """
-        if self.val_table and self.result_table:  # Log Table if Val dataset is uploaded as artifact
-            self.log_training_progress(predn, path, names)
-
-        if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
-            if self.current_epoch % self.bbox_interval == 0:
-                box_data = [{
-                    "position": {
-                        "minX": xyxy[0],
-                        "minY": xyxy[1],
-                        "maxX": xyxy[2],
-                        "maxY": xyxy[3]},
-                    "class_id": int(cls),
-                    "box_caption": f"{names[int(cls)]} {conf:.3f}",
-                    "scores": {
-                        "class_score": conf},
-                    "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
-                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-                self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
-
-    def log(self, log_dict):
-        """
-        save the metrics to the logging dictionary
-
-        arguments:
-        log_dict (Dict) -- metrics/media to be logged in current step
-        """
-        if self.wandb_run:
-            for key, value in log_dict.items():
-                self.log_dict[key] = value
-
-    def end_epoch(self, best_result=False):
-        """
-        commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
-
-        arguments:
-        best_result (boolean): Boolean representing if the result of this evaluation is best or not
-        """
-        if self.wandb_run:
-            with all_logging_disabled():
-                if self.bbox_media_panel_images:
-                    self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
-                try:
-                    wandb.log(self.log_dict)
-                except BaseException as e:
-                    LOGGER.info(
-                        f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
-                    )
-                    self.wandb_run.finish()
-                    self.wandb_run = None
-
-                self.log_dict = {}
-                self.bbox_media_panel_images = []
-            if self.result_artifact:
-                self.result_artifact.add(self.result_table, 'result')
-                wandb.log_artifact(self.result_artifact,
-                                   aliases=[
-                                       'latest', 'last', 'epoch ' + str(self.current_epoch),
-                                       ('best' if best_result else '')])
-
-                wandb.log({"evaluation": self.result_table})
-                columns = ["epoch", "id", "ground truth", "prediction"]
-                columns.extend(self.data_dict['names'])
-                self.result_table = wandb.Table(columns)
-                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-
-    def finish_run(self):
-        """
-        Log metrics if any and finish the current W&B run
-        """
-        if self.wandb_run:
-            if self.log_dict:
-                with all_logging_disabled():
-                    wandb.log(self.log_dict)
-            wandb.run.finish()
-
-
-@contextmanager
-def all_logging_disabled(highest_level=logging.CRITICAL):
-    """ source - https://gist.github.com/simon-weber/7853144
-    A context manager that will prevent any logging messages triggered during the body from being processed.
-    :param highest_level: the maximum logging level in use.
-      This would only need to be changed if a custom level greater than CRITICAL is defined.
-    """
-    previous_level = logging.root.manager.disable
-    logging.disable(highest_level)
-    try:
-        yield
-    finally:
-        logging.disable(previous_level)

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/util.py

@@ -1,1932 +0,0 @@
-#
-# Copyright (C) 2012-2021 The Python Software Foundation.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-import codecs
-from collections import deque
-import contextlib
-import csv
-from glob import iglob as std_iglob
-import io
-import json
-import logging
-import os
-import py_compile
-import re
-import socket
-try:
-    import ssl
-except ImportError:  # pragma: no cover
-    ssl = None
-import subprocess
-import sys
-import tarfile
-import tempfile
-import textwrap
-
-try:
-    import threading
-except ImportError:  # pragma: no cover
-    import dummy_threading as threading
-import time
-
-from . import DistlibException
-from .compat import (string_types, text_type, shutil, raw_input, StringIO,
-                     cache_from_source, urlopen, urljoin, httplib, xmlrpclib,
-                     splittype, HTTPHandler, BaseConfigurator, valid_ident,
-                     Container, configparser, URLError, ZipFile, fsdecode,
-                     unquote, urlparse)
-
-logger = logging.getLogger(__name__)
-
-#
-# Requirement parsing code as per PEP 508
-#
-
-IDENTIFIER = re.compile(r'^([\w\.-]+)\s*')
-VERSION_IDENTIFIER = re.compile(r'^([\w\.*+-]+)\s*')
-COMPARE_OP = re.compile(r'^(<=?|>=?|={2,3}|[~!]=)\s*')
-MARKER_OP = re.compile(r'^((<=?)|(>=?)|={2,3}|[~!]=|in|not\s+in)\s*')
-OR = re.compile(r'^or\b\s*')
-AND = re.compile(r'^and\b\s*')
-NON_SPACE = re.compile(r'(\S+)\s*')
-STRING_CHUNK = re.compile(r'([\s\w\.{}()*+#:;,/?!~`@$%^&=|<>\[\]-]+)')
-
-
-def parse_marker(marker_string):
-    """
-    Parse a marker string and return a dictionary containing a marker expression.
-
-    The dictionary will contain keys "op", "lhs" and "rhs" for non-terminals in
-    the expression grammar, or strings. A string contained in quotes is to be
-    interpreted as a literal string, and a string not contained in quotes is a
-    variable (such as os_name).
-    """
-    def marker_var(remaining):
-        # either identifier, or literal string
-        m = IDENTIFIER.match(remaining)
-        if m:
-            result = m.groups()[0]
-            remaining = remaining[m.end():]
-        elif not remaining:
-            raise SyntaxError('unexpected end of input')
-        else:
-            q = remaining[0]
-            if q not in '\'"':
-                raise SyntaxError('invalid expression: %s' % remaining)
-            oq = '\'"'.replace(q, '')
-            remaining = remaining[1:]
-            parts = [q]
-            while remaining:
-                # either a string chunk, or oq, or q to terminate
-                if remaining[0] == q:
-                    break
-                elif remaining[0] == oq:
-                    parts.append(oq)
-                    remaining = remaining[1:]
-                else:
-                    m = STRING_CHUNK.match(remaining)
-                    if not m:
-                        raise SyntaxError('error in string literal: %s' % remaining)
-                    parts.append(m.groups()[0])
-                    remaining = remaining[m.end():]
-            else:
-                s = ''.join(parts)
-                raise SyntaxError('unterminated string: %s' % s)
-            parts.append(q)
-            result = ''.join(parts)
-            remaining = remaining[1:].lstrip() # skip past closing quote
-        return result, remaining
-
-    def marker_expr(remaining):
-        if remaining and remaining[0] == '(':
-            result, remaining = marker(remaining[1:].lstrip())
-            if remaining[0] != ')':
-                raise SyntaxError('unterminated parenthesis: %s' % remaining)
-            remaining = remaining[1:].lstrip()
-        else:
-            lhs, remaining = marker_var(remaining)
-            while remaining:
-                m = MARKER_OP.match(remaining)
-                if not m:
-                    break
-                op = m.groups()[0]
-                remaining = remaining[m.end():]
-                rhs, remaining = marker_var(remaining)
-                lhs = {'op': op, 'lhs': lhs, 'rhs': rhs}
-            result = lhs
-        return result, remaining
-
-    def marker_and(remaining):
-        lhs, remaining = marker_expr(remaining)
-        while remaining:
-            m = AND.match(remaining)
-            if not m:
-                break
-            remaining = remaining[m.end():]
-            rhs, remaining = marker_expr(remaining)
-            lhs = {'op': 'and', 'lhs': lhs, 'rhs': rhs}
-        return lhs, remaining
-
-    def marker(remaining):
-        lhs, remaining = marker_and(remaining)
-        while remaining:
-            m = OR.match(remaining)
-            if not m:
-                break
-            remaining = remaining[m.end():]
-            rhs, remaining = marker_and(remaining)
-            lhs = {'op': 'or', 'lhs': lhs, 'rhs': rhs}
-        return lhs, remaining
-
-    return marker(marker_string)
-
-
-def parse_requirement(req):
-    """
-    Parse a requirement passed in as a string. Return a Container
-    whose attributes contain the various parts of the requirement.
-    """
-    remaining = req.strip()
-    if not remaining or remaining.startswith('#'):
-        return None
-    m = IDENTIFIER.match(remaining)
-    if not m:
-        raise SyntaxError('name expected: %s' % remaining)
-    distname = m.groups()[0]
-    remaining = remaining[m.end():]
-    extras = mark_expr = versions = uri = None
-    if remaining and remaining[0] == '[':
-        i = remaining.find(']', 1)
-        if i < 0:
-            raise SyntaxError('unterminated extra: %s' % remaining)
-        s = remaining[1:i]
-        remaining = remaining[i + 1:].lstrip()
-        extras = []
-        while s:
-            m = IDENTIFIER.match(s)
-            if not m:
-                raise SyntaxError('malformed extra: %s' % s)
-            extras.append(m.groups()[0])
-            s = s[m.end():]
-            if not s:
-                break
-            if s[0] != ',':
-                raise SyntaxError('comma expected in extras: %s' % s)
-            s = s[1:].lstrip()
-        if not extras:
-            extras = None
-    if remaining:
-        if remaining[0] == '@':
-            # it's a URI
-            remaining = remaining[1:].lstrip()
-            m = NON_SPACE.match(remaining)
-            if not m:
-                raise SyntaxError('invalid URI: %s' % remaining)
-            uri = m.groups()[0]
-            t = urlparse(uri)
-            # there are issues with Python and URL parsing, so this test
-            # is a bit crude. See bpo-20271, bpo-23505. Python doesn't
-            # always parse invalid URLs correctly - it should raise
-            # exceptions for malformed URLs
-            if not (t.scheme and t.netloc):
-                raise SyntaxError('Invalid URL: %s' % uri)
-            remaining = remaining[m.end():].lstrip()
-        else:
-
-            def get_versions(ver_remaining):
-                """
-                Return a list of operator, version tuples if any are
-                specified, else None.
-                """
-                m = COMPARE_OP.match(ver_remaining)
-                versions = None
-                if m:
-                    versions = []
-                    while True:
-                        op = m.groups()[0]
-                        ver_remaining = ver_remaining[m.end():]
-                        m = VERSION_IDENTIFIER.match(ver_remaining)
-                        if not m:
-                            raise SyntaxError('invalid version: %s' % ver_remaining)
-                        v = m.groups()[0]
-                        versions.append((op, v))
-                        ver_remaining = ver_remaining[m.end():]
-                        if not ver_remaining or ver_remaining[0] != ',':
-                            break
-                        ver_remaining = ver_remaining[1:].lstrip()
-                        # Some packages have a trailing comma which would break things
-                        # See issue #148
-                        if not ver_remaining:
-                            break
-                        m = COMPARE_OP.match(ver_remaining)
-                        if not m:
-                            raise SyntaxError('invalid constraint: %s' % ver_remaining)
-                    if not versions:
-                        versions = None
-                return versions, ver_remaining
-
-            if remaining[0] != '(':
-                versions, remaining = get_versions(remaining)
-            else:
-                i = remaining.find(')', 1)
-                if i < 0:
-                    raise SyntaxError('unterminated parenthesis: %s' % remaining)
-                s = remaining[1:i]
-                remaining = remaining[i + 1:].lstrip()
-                # As a special diversion from PEP 508, allow a version number
-                # a.b.c in parentheses as a synonym for ~= a.b.c (because this
-                # is allowed in earlier PEPs)
-                if COMPARE_OP.match(s):
-                    versions, _ = get_versions(s)
-                else:
-                    m = VERSION_IDENTIFIER.match(s)
-                    if not m:
-                        raise SyntaxError('invalid constraint: %s' % s)
-                    v = m.groups()[0]
-                    s = s[m.end():].lstrip()
-                    if s:
-                        raise SyntaxError('invalid constraint: %s' % s)
-                    versions = [('~=', v)]
-
-    if remaining:
-        if remaining[0] != ';':
-            raise SyntaxError('invalid requirement: %s' % remaining)
-        remaining = remaining[1:].lstrip()
-
-        mark_expr, remaining = parse_marker(remaining)
-
-    if remaining and remaining[0] != '#':
-        raise SyntaxError('unexpected trailing data: %s' % remaining)
-
-    if not versions:
-        rs = distname
-    else:
-        rs = '%s %s' % (distname, ', '.join(['%s %s' % con for con in versions]))
-    return Container(name=distname, extras=extras, constraints=versions,
-                     marker=mark_expr, url=uri, requirement=rs)
-
-
-def get_resources_dests(resources_root, rules):
-    """Find destinations for resources files"""
-
-    def get_rel_path(root, path):
-        # normalizes and returns a lstripped-/-separated path
-        root = root.replace(os.path.sep, '/')
-        path = path.replace(os.path.sep, '/')
-        assert path.startswith(root)
-        return path[len(root):].lstrip('/')
-
-    destinations = {}
-    for base, suffix, dest in rules:
-        prefix = os.path.join(resources_root, base)
-        for abs_base in iglob(prefix):
-            abs_glob = os.path.join(abs_base, suffix)
-            for abs_path in iglob(abs_glob):
-                resource_file = get_rel_path(resources_root, abs_path)
-                if dest is None:  # remove the entry if it was here
-                    destinations.pop(resource_file, None)
-                else:
-                    rel_path = get_rel_path(abs_base, abs_path)
-                    rel_dest = dest.replace(os.path.sep, '/').rstrip('/')
-                    destinations[resource_file] = rel_dest + '/' + rel_path
-    return destinations
-
-
-def in_venv():
-    if hasattr(sys, 'real_prefix'):
-        # virtualenv venvs
-        result = True
-    else:
-        # PEP 405 venvs
-        result = sys.prefix != getattr(sys, 'base_prefix', sys.prefix)
-    return result
-
-
-def get_executable():
-# The __PYVENV_LAUNCHER__ dance is apparently no longer needed, as
-# changes to the stub launcher mean that sys.executable always points
-# to the stub on OS X
-#    if sys.platform == 'darwin' and ('__PYVENV_LAUNCHER__'
-#                                     in os.environ):
-#        result =  os.environ['__PYVENV_LAUNCHER__']
-#    else:
-#        result = sys.executable
-#    return result
-    # Avoid normcasing: see issue #143
-    # result = os.path.normcase(sys.executable)
-    result = sys.executable
-    if not isinstance(result, text_type):
-        result = fsdecode(result)
-    return result
-
-
-def proceed(prompt, allowed_chars, error_prompt=None, default=None):
-    p = prompt
-    while True:
-        s = raw_input(p)
-        p = prompt
-        if not s and default:
-            s = default
-        if s:
-            c = s[0].lower()
-            if c in allowed_chars:
-                break
-            if error_prompt:
-                p = '%c: %s\n%s' % (c, error_prompt, prompt)
-    return c
-
-
-def extract_by_key(d, keys):
-    if isinstance(keys, string_types):
-        keys = keys.split()
-    result = {}
-    for key in keys:
-        if key in d:
-            result[key] = d[key]
-    return result
-
-def read_exports(stream):
-    if sys.version_info[0] >= 3:
-        # needs to be a text stream
-        stream = codecs.getreader('utf-8')(stream)
-    # Try to load as JSON, falling back on legacy format
-    data = stream.read()
-    stream = StringIO(data)
-    try:
-        jdata = json.load(stream)
-        result = jdata['extensions']['python.exports']['exports']
-        for group, entries in result.items():
-            for k, v in entries.items():
-                s = '%s = %s' % (k, v)
-                entry = get_export_entry(s)
-                assert entry is not None
-                entries[k] = entry
-        return result
-    except Exception:
-        stream.seek(0, 0)
-
-    def read_stream(cp, stream):
-        if hasattr(cp, 'read_file'):
-            cp.read_file(stream)
-        else:
-            cp.readfp(stream)
-
-    cp = configparser.ConfigParser()
-    try:
-        read_stream(cp, stream)
-    except configparser.MissingSectionHeaderError:
-        stream.close()
-        data = textwrap.dedent(data)
-        stream = StringIO(data)
-        read_stream(cp, stream)
-
-    result = {}
-    for key in cp.sections():
-        result[key] = entries = {}
-        for name, value in cp.items(key):
-            s = '%s = %s' % (name, value)
-            entry = get_export_entry(s)
-            assert entry is not None
-            #entry.dist = self
-            entries[name] = entry
-    return result
-
-
-def write_exports(exports, stream):
-    if sys.version_info[0] >= 3:
-        # needs to be a text stream
-        stream = codecs.getwriter('utf-8')(stream)
-    cp = configparser.ConfigParser()
-    for k, v in exports.items():
-        # TODO check k, v for valid values
-        cp.add_section(k)
-        for entry in v.values():
-            if entry.suffix is None:
-                s = entry.prefix
-            else:
-                s = '%s:%s' % (entry.prefix, entry.suffix)
-            if entry.flags:
-                s = '%s [%s]' % (s, ', '.join(entry.flags))
-            cp.set(k, entry.name, s)
-    cp.write(stream)
-
-
-@contextlib.contextmanager
-def tempdir():
-    td = tempfile.mkdtemp()
-    try:
-        yield td
-    finally:
-        shutil.rmtree(td)
-
-@contextlib.contextmanager
-def chdir(d):
-    cwd = os.getcwd()
-    try:
-        os.chdir(d)
-        yield
-    finally:
-        os.chdir(cwd)
-
-
-@contextlib.contextmanager
-def socket_timeout(seconds=15):
-    cto = socket.getdefaulttimeout()
-    try:
-        socket.setdefaulttimeout(seconds)
-        yield
-    finally:
-        socket.setdefaulttimeout(cto)
-
-
-class cached_property(object):
-    def __init__(self, func):
-        self.func = func
-        #for attr in ('__name__', '__module__', '__doc__'):
-        #    setattr(self, attr, getattr(func, attr, None))
-
-    def __get__(self, obj, cls=None):
-        if obj is None:
-            return self
-        value = self.func(obj)
-        object.__setattr__(obj, self.func.__name__, value)
-        #obj.__dict__[self.func.__name__] = value = self.func(obj)
-        return value
-
-def convert_path(pathname):
-    """Return 'pathname' as a name that will work on the native filesystem.
-
-    The path is split on '/' and put back together again using the current
-    directory separator.  Needed because filenames in the setup script are
-    always supplied in Unix style, and have to be converted to the local
-    convention before we can actually use them in the filesystem.  Raises
-    ValueError on non-Unix-ish systems if 'pathname' either starts or
-    ends with a slash.
-    """
-    if os.sep == '/':
-        return pathname
-    if not pathname:
-        return pathname
-    if pathname[0] == '/':
-        raise ValueError("path '%s' cannot be absolute" % pathname)
-    if pathname[-1] == '/':
-        raise ValueError("path '%s' cannot end with '/'" % pathname)
-
-    paths = pathname.split('/')
-    while os.curdir in paths:
-        paths.remove(os.curdir)
-    if not paths:
-        return os.curdir
-    return os.path.join(*paths)
-
-
-class FileOperator(object):
-    def __init__(self, dry_run=False):
-        self.dry_run = dry_run
-        self.ensured = set()
-        self._init_record()
-
-    def _init_record(self):
-        self.record = False
-        self.files_written = set()
-        self.dirs_created = set()
-
-    def record_as_written(self, path):
-        if self.record:
-            self.files_written.add(path)
-
-    def newer(self, source, target):
-        """Tell if the target is newer than the source.
-
-        Returns true if 'source' exists and is more recently modified than
-        'target', or if 'source' exists and 'target' doesn't.
-
-        Returns false if both exist and 'target' is the same age or younger
-        than 'source'. Raise PackagingFileError if 'source' does not exist.
-
-        Note that this test is not very accurate: files created in the same
-        second will have the same "age".
-        """
-        if not os.path.exists(source):
-            raise DistlibException("file '%r' does not exist" %
-                                   os.path.abspath(source))
-        if not os.path.exists(target):
-            return True
-
-        return os.stat(source).st_mtime > os.stat(target).st_mtime
-
-    def copy_file(self, infile, outfile, check=True):
-        """Copy a file respecting dry-run and force flags.
-        """
-        self.ensure_dir(os.path.dirname(outfile))
-        logger.info('Copying %s to %s', infile, outfile)
-        if not self.dry_run:
-            msg = None
-            if check:
-                if os.path.islink(outfile):
-                    msg = '%s is a symlink' % outfile
-                elif os.path.exists(outfile) and not os.path.isfile(outfile):
-                    msg = '%s is a non-regular file' % outfile
-            if msg:
-                raise ValueError(msg + ' which would be overwritten')
-            shutil.copyfile(infile, outfile)
-        self.record_as_written(outfile)
-
-    def copy_stream(self, instream, outfile, encoding=None):
-        assert not os.path.isdir(outfile)
-        self.ensure_dir(os.path.dirname(outfile))
-        logger.info('Copying stream %s to %s', instream, outfile)
-        if not self.dry_run:
-            if encoding is None:
-                outstream = open(outfile, 'wb')
-            else:
-                outstream = codecs.open(outfile, 'w', encoding=encoding)
-            try:
-                shutil.copyfileobj(instream, outstream)
-            finally:
-                outstream.close()
-        self.record_as_written(outfile)
-
-    def write_binary_file(self, path, data):
-        self.ensure_dir(os.path.dirname(path))
-        if not self.dry_run:
-            if os.path.exists(path):
-                os.remove(path)
-            with open(path, 'wb') as f:
-                f.write(data)
-        self.record_as_written(path)
-
-    def write_text_file(self, path, data, encoding):
-        self.write_binary_file(path, data.encode(encoding))
-
-    def set_mode(self, bits, mask, files):
-        if os.name == 'posix' or (os.name == 'java' and os._name == 'posix'):
-            # Set the executable bits (owner, group, and world) on
-            # all the files specified.
-            for f in files:
-                if self.dry_run:
-                    logger.info("changing mode of %s", f)
-                else:
-                    mode = (os.stat(f).st_mode | bits) & mask
-                    logger.info("changing mode of %s to %o", f, mode)
-                    os.chmod(f, mode)
-
-    set_executable_mode = lambda s, f: s.set_mode(0o555, 0o7777, f)
-
-    def ensure_dir(self, path):
-        path = os.path.abspath(path)
-        if path not in self.ensured and not os.path.exists(path):
-            self.ensured.add(path)
-            d, f = os.path.split(path)
-            self.ensure_dir(d)
-            logger.info('Creating %s' % path)
-            if not self.dry_run:
-                os.mkdir(path)
-            if self.record:
-                self.dirs_created.add(path)
-
-    def byte_compile(self, path, optimize=False, force=False, prefix=None, hashed_invalidation=False):
-        dpath = cache_from_source(path, not optimize)
-        logger.info('Byte-compiling %s to %s', path, dpath)
-        if not self.dry_run:
-            if force or self.newer(path, dpath):
-                if not prefix:
-                    diagpath = None
-                else:
-                    assert path.startswith(prefix)
-                    diagpath = path[len(prefix):]
-            compile_kwargs = {}
-            if hashed_invalidation and hasattr(py_compile, 'PycInvalidationMode'):
-                compile_kwargs['invalidation_mode'] = py_compile.PycInvalidationMode.CHECKED_HASH
-            py_compile.compile(path, dpath, diagpath, True, **compile_kwargs)     # raise error
-        self.record_as_written(dpath)
-        return dpath
-
-    def ensure_removed(self, path):
-        if os.path.exists(path):
-            if os.path.isdir(path) and not os.path.islink(path):
-                logger.debug('Removing directory tree at %s', path)
-                if not self.dry_run:
-                    shutil.rmtree(path)
-                if self.record:
-                    if path in self.dirs_created:
-                        self.dirs_created.remove(path)
-            else:
-                if os.path.islink(path):
-                    s = 'link'
-                else:
-                    s = 'file'
-                logger.debug('Removing %s %s', s, path)
-                if not self.dry_run:
-                    os.remove(path)
-                if self.record:
-                    if path in self.files_written:
-                        self.files_written.remove(path)
-
-    def is_writable(self, path):
-        result = False
-        while not result:
-            if os.path.exists(path):
-                result = os.access(path, os.W_OK)
-                break
-            parent = os.path.dirname(path)
-            if parent == path:
-                break
-            path = parent
-        return result
-
-    def commit(self):
-        """
-        Commit recorded changes, turn off recording, return
-        changes.
-        """
-        assert self.record
-        result = self.files_written, self.dirs_created
-        self._init_record()
-        return result
-
-    def rollback(self):
-        if not self.dry_run:
-            for f in list(self.files_written):
-                if os.path.exists(f):
-                    os.remove(f)
-            # dirs should all be empty now, except perhaps for
-            # __pycache__ subdirs
-            # reverse so that subdirs appear before their parents
-            dirs = sorted(self.dirs_created, reverse=True)
-            for d in dirs:
-                flist = os.listdir(d)
-                if flist:
-                    assert flist == ['__pycache__']
-                    sd = os.path.join(d, flist[0])
-                    os.rmdir(sd)
-                os.rmdir(d)     # should fail if non-empty
-        self._init_record()
-
-def resolve(module_name, dotted_path):
-    if module_name in sys.modules:
-        mod = sys.modules[module_name]
-    else:
-        mod = __import__(module_name)
-    if dotted_path is None:
-        result = mod
-    else:
-        parts = dotted_path.split('.')
-        result = getattr(mod, parts.pop(0))
-        for p in parts:
-            result = getattr(result, p)
-    return result
-
-
-class ExportEntry(object):
-    def __init__(self, name, prefix, suffix, flags):
-        self.name = name
-        self.prefix = prefix
-        self.suffix = suffix
-        self.flags = flags
-
-    @cached_property
-    def value(self):
-        return resolve(self.prefix, self.suffix)
-
-    def __repr__(self):  # pragma: no cover
-        return '<ExportEntry %s = %s:%s %s>' % (self.name, self.prefix,
-                                                self.suffix, self.flags)
-
-    def __eq__(self, other):
-        if not isinstance(other, ExportEntry):
-            result = False
-        else:
-            result = (self.name == other.name and
-                      self.prefix == other.prefix and
-                      self.suffix == other.suffix and
-                      self.flags == other.flags)
-        return result
-
-    __hash__ = object.__hash__
-
-
-ENTRY_RE = re.compile(r'''(?P<name>(\w|[-.+])+)
-                      \s*=\s*(?P<callable>(\w+)([:\.]\w+)*)
-                      \s*(\[\s*(?P<flags>[\w-]+(=\w+)?(,\s*\w+(=\w+)?)*)\s*\])?
-                      ''', re.VERBOSE)
-
-def get_export_entry(specification):
-    m = ENTRY_RE.search(specification)
-    if not m:
-        result = None
-        if '[' in specification or ']' in specification:
-            raise DistlibException("Invalid specification "
-                                   "'%s'" % specification)
-    else:
-        d = m.groupdict()
-        name = d['name']
-        path = d['callable']
-        colons = path.count(':')
-        if colons == 0:
-            prefix, suffix = path, None
-        else:
-            if colons != 1:
-                raise DistlibException("Invalid specification "
-                                       "'%s'" % specification)
-            prefix, suffix = path.split(':')
-        flags = d['flags']
-        if flags is None:
-            if '[' in specification or ']' in specification:
-                raise DistlibException("Invalid specification "
-                                       "'%s'" % specification)
-            flags = []
-        else:
-            flags = [f.strip() for f in flags.split(',')]
-        result = ExportEntry(name, prefix, suffix, flags)
-    return result
-
-
-def get_cache_base(suffix=None):
-    """
-    Return the default base location for distlib caches. If the directory does
-    not exist, it is created. Use the suffix provided for the base directory,
-    and default to '.distlib' if it isn't provided.
-
-    On Windows, if LOCALAPPDATA is defined in the environment, then it is
-    assumed to be a directory, and will be the parent directory of the result.
-    On POSIX, and on Windows if LOCALAPPDATA is not defined, the user's home
-    directory - using os.expanduser('~') - will be the parent directory of
-    the result.
-
-    The result is just the directory '.distlib' in the parent directory as
-    determined above, or with the name specified with ``suffix``.
-    """
-    if suffix is None:
-        suffix = '.distlib'
-    if os.name == 'nt' and 'LOCALAPPDATA' in os.environ:
-        result = os.path.expandvars('$localappdata')
-    else:
-        # Assume posix, or old Windows
-        result = os.path.expanduser('~')
-    # we use 'isdir' instead of 'exists', because we want to
-    # fail if there's a file with that name
-    if os.path.isdir(result):
-        usable = os.access(result, os.W_OK)
-        if not usable:
-            logger.warning('Directory exists but is not writable: %s', result)
-    else:
-        try:
-            os.makedirs(result)
-            usable = True
-        except OSError:
-            logger.warning('Unable to create %s', result, exc_info=True)
-            usable = False
-    if not usable:
-        result = tempfile.mkdtemp()
-        logger.warning('Default location unusable, using %s', result)
-    return os.path.join(result, suffix)
-
-
-def path_to_cache_dir(path):
-    """
-    Convert an absolute path to a directory name for use in a cache.
-
-    The algorithm used is:
-
-    #. On Windows, any ``':'`` in the drive is replaced with ``'---'``.
-    #. Any occurrence of ``os.sep`` is replaced with ``'--'``.
-    #. ``'.cache'`` is appended.
-    """
-    d, p = os.path.splitdrive(os.path.abspath(path))
-    if d:
-        d = d.replace(':', '---')
-    p = p.replace(os.sep, '--')
-    return d + p + '.cache'
-
-
-def ensure_slash(s):
-    if not s.endswith('/'):
-        return s + '/'
-    return s
-
-
-def parse_credentials(netloc):
-    username = password = None
-    if '@' in netloc:
-        prefix, netloc = netloc.rsplit('@', 1)
-        if ':' not in prefix:
-            username = prefix
-        else:
-            username, password = prefix.split(':', 1)
-    if username:
-        username = unquote(username)
-    if password:
-        password = unquote(password)
-    return username, password, netloc
-
-
-def get_process_umask():
-    result = os.umask(0o22)
-    os.umask(result)
-    return result
-
-def is_string_sequence(seq):
-    result = True
-    i = None
-    for i, s in enumerate(seq):
-        if not isinstance(s, string_types):
-            result = False
-            break
-    assert i is not None
-    return result
-
-PROJECT_NAME_AND_VERSION = re.compile('([a-z0-9_]+([.-][a-z_][a-z0-9_]*)*)-'
-                                      '([a-z0-9_.+-]+)', re.I)
-PYTHON_VERSION = re.compile(r'-py(\d\.?\d?)')
-
-
-def split_filename(filename, project_name=None):
-    """
-    Extract name, version, python version from a filename (no extension)
-
-    Return name, version, pyver or None
-    """
-    result = None
-    pyver = None
-    filename = unquote(filename).replace(' ', '-')
-    m = PYTHON_VERSION.search(filename)
-    if m:
-        pyver = m.group(1)
-        filename = filename[:m.start()]
-    if project_name and len(filename) > len(project_name) + 1:
-        m = re.match(re.escape(project_name) + r'\b', filename)
-        if m:
-            n = m.end()
-            result = filename[:n], filename[n + 1:], pyver
-    if result is None:
-        m = PROJECT_NAME_AND_VERSION.match(filename)
-        if m:
-            result = m.group(1), m.group(3), pyver
-    return result
-
-# Allow spaces in name because of legacy dists like "Twisted Core"
-NAME_VERSION_RE = re.compile(r'(?P<name>[\w .-]+)\s*'
-                             r'\(\s*(?P<ver>[^\s)]+)\)$')
-
-def parse_name_and_version(p):
-    """
-    A utility method used to get name and version from a string.
-
-    From e.g. a Provides-Dist value.
-
-    :param p: A value in a form 'foo (1.0)'
-    :return: The name and version as a tuple.
-    """
-    m = NAME_VERSION_RE.match(p)
-    if not m:
-        raise DistlibException('Ill-formed name/version string: \'%s\'' % p)
-    d = m.groupdict()
-    return d['name'].strip().lower(), d['ver']
-
-def get_extras(requested, available):
-    result = set()
-    requested = set(requested or [])
-    available = set(available or [])
-    if '*' in requested:
-        requested.remove('*')
-        result |= available
-    for r in requested:
-        if r == '-':
-            result.add(r)
-        elif r.startswith('-'):
-            unwanted = r[1:]
-            if unwanted not in available:
-                logger.warning('undeclared extra: %s' % unwanted)
-            if unwanted in result:
-                result.remove(unwanted)
-        else:
-            if r not in available:
-                logger.warning('undeclared extra: %s' % r)
-            result.add(r)
-    return result
-#
-# Extended metadata functionality
-#
-
-def _get_external_data(url):
-    result = {}
-    try:
-        # urlopen might fail if it runs into redirections,
-        # because of Python issue #13696. Fixed in locators
-        # using a custom redirect handler.
-        resp = urlopen(url)
-        headers = resp.info()
-        ct = headers.get('Content-Type')
-        if not ct.startswith('application/json'):
-            logger.debug('Unexpected response for JSON request: %s', ct)
-        else:
-            reader = codecs.getreader('utf-8')(resp)
-            #data = reader.read().decode('utf-8')
-            #result = json.loads(data)
-            result = json.load(reader)
-    except Exception as e:
-        logger.exception('Failed to get external data for %s: %s', url, e)
-    return result
-
-_external_data_base_url = 'https://www.red-dove.com/pypi/projects/'
-
-def get_project_data(name):
-    url = '%s/%s/project.json' % (name[0].upper(), name)
-    url = urljoin(_external_data_base_url, url)
-    result = _get_external_data(url)
-    return result
-
-def get_package_data(name, version):
-    url = '%s/%s/package-%s.json' % (name[0].upper(), name, version)
-    url = urljoin(_external_data_base_url, url)
-    return _get_external_data(url)
-
-
-class Cache(object):
-    """
-    A class implementing a cache for resources that need to live in the file system
-    e.g. shared libraries. This class was moved from resources to here because it
-    could be used by other modules, e.g. the wheel module.
-    """
-
-    def __init__(self, base):
-        """
-        Initialise an instance.
-
-        :param base: The base directory where the cache should be located.
-        """
-        # we use 'isdir' instead of 'exists', because we want to
-        # fail if there's a file with that name
-        if not os.path.isdir(base):  # pragma: no cover
-            os.makedirs(base)
-        if (os.stat(base).st_mode & 0o77) != 0:
-            logger.warning('Directory \'%s\' is not private', base)
-        self.base = os.path.abspath(os.path.normpath(base))
-
-    def prefix_to_dir(self, prefix):
-        """
-        Converts a resource prefix to a directory name in the cache.
-        """
-        return path_to_cache_dir(prefix)
-
-    def clear(self):
-        """
-        Clear the cache.
-        """
-        not_removed = []
-        for fn in os.listdir(self.base):
-            fn = os.path.join(self.base, fn)
-            try:
-                if os.path.islink(fn) or os.path.isfile(fn):
-                    os.remove(fn)
-                elif os.path.isdir(fn):
-                    shutil.rmtree(fn)
-            except Exception:
-                not_removed.append(fn)
-        return not_removed
-
-
-class EventMixin(object):
-    """
-    A very simple publish/subscribe system.
-    """
-    def __init__(self):
-        self._subscribers = {}
-
-    def add(self, event, subscriber, append=True):
-        """
-        Add a subscriber for an event.
-
-        :param event: The name of an event.
-        :param subscriber: The subscriber to be added (and called when the
-                           event is published).
-        :param append: Whether to append or prepend the subscriber to an
-                       existing subscriber list for the event.
-        """
-        subs = self._subscribers
-        if event not in subs:
-            subs[event] = deque([subscriber])
-        else:
-            sq = subs[event]
-            if append:
-                sq.append(subscriber)
-            else:
-                sq.appendleft(subscriber)
-
-    def remove(self, event, subscriber):
-        """
-        Remove a subscriber for an event.
-
-        :param event: The name of an event.
-        :param subscriber: The subscriber to be removed.
-        """
-        subs = self._subscribers
-        if event not in subs:
-            raise ValueError('No subscribers: %r' % event)
-        subs[event].remove(subscriber)
-
-    def get_subscribers(self, event):
-        """
-        Return an iterator for the subscribers for an event.
-        :param event: The event to return subscribers for.
-        """
-        return iter(self._subscribers.get(event, ()))
-
-    def publish(self, event, *args, **kwargs):
-        """
-        Publish a event and return a list of values returned by its
-        subscribers.
-
-        :param event: The event to publish.
-        :param args: The positional arguments to pass to the event's
-                     subscribers.
-        :param kwargs: The keyword arguments to pass to the event's
-                       subscribers.
-        """
-        result = []
-        for subscriber in self.get_subscribers(event):
-            try:
-                value = subscriber(event, *args, **kwargs)
-            except Exception:
-                logger.exception('Exception during event publication')
-                value = None
-            result.append(value)
-        logger.debug('publish %s: args = %s, kwargs = %s, result = %s',
-                     event, args, kwargs, result)
-        return result
-
-#
-# Simple sequencing
-#
-class Sequencer(object):
-    def __init__(self):
-        self._preds = {}
-        self._succs = {}
-        self._nodes = set()     # nodes with no preds/succs
-
-    def add_node(self, node):
-        self._nodes.add(node)
-
-    def remove_node(self, node, edges=False):
-        if node in self._nodes:
-            self._nodes.remove(node)
-        if edges:
-            for p in set(self._preds.get(node, ())):
-                self.remove(p, node)
-            for s in set(self._succs.get(node, ())):
-                self.remove(node, s)
-            # Remove empties
-            for k, v in list(self._preds.items()):
-                if not v:
-                    del self._preds[k]
-            for k, v in list(self._succs.items()):
-                if not v:
-                    del self._succs[k]
-
-    def add(self, pred, succ):
-        assert pred != succ
-        self._preds.setdefault(succ, set()).add(pred)
-        self._succs.setdefault(pred, set()).add(succ)
-
-    def remove(self, pred, succ):
-        assert pred != succ
-        try:
-            preds = self._preds[succ]
-            succs = self._succs[pred]
-        except KeyError:  # pragma: no cover
-            raise ValueError('%r not a successor of anything' % succ)
-        try:
-            preds.remove(pred)
-            succs.remove(succ)
-        except KeyError:  # pragma: no cover
-            raise ValueError('%r not a successor of %r' % (succ, pred))
-
-    def is_step(self, step):
-        return (step in self._preds or step in self._succs or
-                step in self._nodes)
-
-    def get_steps(self, final):
-        if not self.is_step(final):
-            raise ValueError('Unknown: %r' % final)
-        result = []
-        todo = []
-        seen = set()
-        todo.append(final)
-        while todo:
-            step = todo.pop(0)
-            if step in seen:
-                # if a step was already seen,
-                # move it to the end (so it will appear earlier
-                # when reversed on return) ... but not for the
-                # final step, as that would be confusing for
-                # users
-                if step != final:
-                    result.remove(step)
-                    result.append(step)
-            else:
-                seen.add(step)
-                result.append(step)
-                preds = self._preds.get(step, ())
-                todo.extend(preds)
-        return reversed(result)
-
-    @property
-    def strong_connections(self):
-        #http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm
-        index_counter = [0]
-        stack = []
-        lowlinks = {}
-        index = {}
-        result = []
-
-        graph = self._succs
-
-        def strongconnect(node):
-            # set the depth index for this node to the smallest unused index
-            index[node] = index_counter[0]
-            lowlinks[node] = index_counter[0]
-            index_counter[0] += 1
-            stack.append(node)
-
-            # Consider successors
-            try:
-                successors = graph[node]
-            except Exception:
-                successors = []
-            for successor in successors:
-                if successor not in lowlinks:
-                    # Successor has not yet been visited
-                    strongconnect(successor)
-                    lowlinks[node] = min(lowlinks[node],lowlinks[successor])
-                elif successor in stack:
-                    # the successor is in the stack and hence in the current
-                    # strongly connected component (SCC)
-                    lowlinks[node] = min(lowlinks[node],index[successor])
-
-            # If `node` is a root node, pop the stack and generate an SCC
-            if lowlinks[node] == index[node]:
-                connected_component = []
-
-                while True:
-                    successor = stack.pop()
-                    connected_component.append(successor)
-                    if successor == node: break
-                component = tuple(connected_component)
-                # storing the result
-                result.append(component)
-
-        for node in graph:
-            if node not in lowlinks:
-                strongconnect(node)
-
-        return result
-
-    @property
-    def dot(self):
-        result = ['digraph G {']
-        for succ in self._preds:
-            preds = self._preds[succ]
-            for pred in preds:
-                result.append('  %s -> %s;' % (pred, succ))
-        for node in self._nodes:
-            result.append('  %s;' % node)
-        result.append('}')
-        return '\n'.join(result)
-
-#
-# Unarchiving functionality for zip, tar, tgz, tbz, whl
-#
-
-ARCHIVE_EXTENSIONS = ('.tar.gz', '.tar.bz2', '.tar', '.zip',
-                      '.tgz', '.tbz', '.whl')
-
-def unarchive(archive_filename, dest_dir, format=None, check=True):
-
-    def check_path(path):
-        if not isinstance(path, text_type):
-            path = path.decode('utf-8')
-        p = os.path.abspath(os.path.join(dest_dir, path))
-        if not p.startswith(dest_dir) or p[plen] != os.sep:
-            raise ValueError('path outside destination: %r' % p)
-
-    dest_dir = os.path.abspath(dest_dir)
-    plen = len(dest_dir)
-    archive = None
-    if format is None:
-        if archive_filename.endswith(('.zip', '.whl')):
-            format = 'zip'
-        elif archive_filename.endswith(('.tar.gz', '.tgz')):
-            format = 'tgz'
-            mode = 'r:gz'
-        elif archive_filename.endswith(('.tar.bz2', '.tbz')):
-            format = 'tbz'
-            mode = 'r:bz2'
-        elif archive_filename.endswith('.tar'):
-            format = 'tar'
-            mode = 'r'
-        else:  # pragma: no cover
-            raise ValueError('Unknown format for %r' % archive_filename)
-    try:
-        if format == 'zip':
-            archive = ZipFile(archive_filename, 'r')
-            if check:
-                names = archive.namelist()
-                for name in names:
-                    check_path(name)
-        else:
-            archive = tarfile.open(archive_filename, mode)
-            if check:
-                names = archive.getnames()
-                for name in names:
-                    check_path(name)
-        if format != 'zip' and sys.version_info[0] < 3:
-            # See Python issue 17153. If the dest path contains Unicode,
-            # tarfile extraction fails on Python 2.x if a member path name
-            # contains non-ASCII characters - it leads to an implicit
-            # bytes -> unicode conversion using ASCII to decode.
-            for tarinfo in archive.getmembers():
-                if not isinstance(tarinfo.name, text_type):
-                    tarinfo.name = tarinfo.name.decode('utf-8')
-        archive.extractall(dest_dir)
-
-    finally:
-        if archive:
-            archive.close()
-
-
-def zip_dir(directory):
-    """zip a directory tree into a BytesIO object"""
-    result = io.BytesIO()
-    dlen = len(directory)
-    with ZipFile(result, "w") as zf:
-        for root, dirs, files in os.walk(directory):
-            for name in files:
-                full = os.path.join(root, name)
-                rel = root[dlen:]
-                dest = os.path.join(rel, name)
-                zf.write(full, dest)
-    return result
-
-#
-# Simple progress bar
-#
-
-UNITS = ('', 'K', 'M', 'G','T','P')
-
-
-class Progress(object):
-    unknown = 'UNKNOWN'
-
-    def __init__(self, minval=0, maxval=100):
-        assert maxval is None or maxval >= minval
-        self.min = self.cur = minval
-        self.max = maxval
-        self.started = None
-        self.elapsed = 0
-        self.done = False
-
-    def update(self, curval):
-        assert self.min <= curval
-        assert self.max is None or curval <= self.max
-        self.cur = curval
-        now = time.time()
-        if self.started is None:
-            self.started = now
-        else:
-            self.elapsed = now - self.started
-
-    def increment(self, incr):
-        assert incr >= 0
-        self.update(self.cur + incr)
-
-    def start(self):
-        self.update(self.min)
-        return self
-
-    def stop(self):
-        if self.max is not None:
-            self.update(self.max)
-        self.done = True
-
-    @property
-    def maximum(self):
-        return self.unknown if self.max is None else self.max
-
-    @property
-    def percentage(self):
-        if self.done:
-            result = '100 %'
-        elif self.max is None:
-            result = ' ?? %'
-        else:
-            v = 100.0 * (self.cur - self.min) / (self.max - self.min)
-            result = '%3d %%' % v
-        return result
-
-    def format_duration(self, duration):
-        if (duration <= 0) and self.max is None or self.cur == self.min:
-            result = '??:??:??'
-        #elif duration < 1:
-        #    result = '--:--:--'
-        else:
-            result = time.strftime('%H:%M:%S', time.gmtime(duration))
-        return result
-
-    @property
-    def ETA(self):
-        if self.done:
-            prefix = 'Done'
-            t = self.elapsed
-            #import pdb; pdb.set_trace()
-        else:
-            prefix = 'ETA '
-            if self.max is None:
-                t = -1
-            elif self.elapsed == 0 or (self.cur == self.min):
-                t = 0
-            else:
-                #import pdb; pdb.set_trace()
-                t = float(self.max - self.min)
-                t /= self.cur - self.min
-                t = (t - 1) * self.elapsed
-        return '%s: %s' % (prefix, self.format_duration(t))
-
-    @property
-    def speed(self):
-        if self.elapsed == 0:
-            result = 0.0
-        else:
-            result = (self.cur - self.min) / self.elapsed
-        for unit in UNITS:
-            if result < 1000:
-                break
-            result /= 1000.0
-        return '%d %sB/s' % (result, unit)
-
-#
-# Glob functionality
-#
-
-RICH_GLOB = re.compile(r'\{([^}]*)\}')
-_CHECK_RECURSIVE_GLOB = re.compile(r'[^/\\,{]\*\*|\*\*[^/\\,}]')
-_CHECK_MISMATCH_SET = re.compile(r'^[^{]*\}|\{[^}]*$')
-
-
-def iglob(path_glob):
-    """Extended globbing function that supports ** and {opt1,opt2,opt3}."""
-    if _CHECK_RECURSIVE_GLOB.search(path_glob):
-        msg = """invalid glob %r: recursive glob "**" must be used alone"""
-        raise ValueError(msg % path_glob)
-    if _CHECK_MISMATCH_SET.search(path_glob):
-        msg = """invalid glob %r: mismatching set marker '{' or '}'"""
-        raise ValueError(msg % path_glob)
-    return _iglob(path_glob)
-
-
-def _iglob(path_glob):
-    rich_path_glob = RICH_GLOB.split(path_glob, 1)
-    if len(rich_path_glob) > 1:
-        assert len(rich_path_glob) == 3, rich_path_glob
-        prefix, set, suffix = rich_path_glob
-        for item in set.split(','):
-            for path in _iglob(''.join((prefix, item, suffix))):
-                yield path
-    else:
-        if '**' not in path_glob:
-            for item in std_iglob(path_glob):
-                yield item
-        else:
-            prefix, radical = path_glob.split('**', 1)
-            if prefix == '':
-                prefix = '.'
-            if radical == '':
-                radical = '*'
-            else:
-                # we support both
-                radical = radical.lstrip('/')
-                radical = radical.lstrip('\\')
-            for path, dir, files in os.walk(prefix):
-                path = os.path.normpath(path)
-                for fn in _iglob(os.path.join(path, radical)):
-                    yield fn
-
-if ssl:
-    from .compat import (HTTPSHandler as BaseHTTPSHandler, match_hostname,
-                         CertificateError)
-
-
-#
-# HTTPSConnection which verifies certificates/matches domains
-#
-
-    class HTTPSConnection(httplib.HTTPSConnection):
-        ca_certs = None # set this to the path to the certs file (.pem)
-        check_domain = True # only used if ca_certs is not None
-
-        # noinspection PyPropertyAccess
-        def connect(self):
-            sock = socket.create_connection((self.host, self.port), self.timeout)
-            if getattr(self, '_tunnel_host', False):
-                self.sock = sock
-                self._tunnel()
-
-            context = ssl.SSLContext(ssl.PROTOCOL_SSLv23)
-            if hasattr(ssl, 'OP_NO_SSLv2'):
-                context.options |= ssl.OP_NO_SSLv2
-            if self.cert_file:
-                context.load_cert_chain(self.cert_file, self.key_file)
-            kwargs = {}
-            if self.ca_certs:
-                context.verify_mode = ssl.CERT_REQUIRED
-                context.load_verify_locations(cafile=self.ca_certs)
-                if getattr(ssl, 'HAS_SNI', False):
-                    kwargs['server_hostname'] = self.host
-
-            self.sock = context.wrap_socket(sock, **kwargs)
-            if self.ca_certs and self.check_domain:
-                try:
-                    match_hostname(self.sock.getpeercert(), self.host)
-                    logger.debug('Host verified: %s', self.host)
-                except CertificateError:  # pragma: no cover
-                    self.sock.shutdown(socket.SHUT_RDWR)
-                    self.sock.close()
-                    raise
-
-    class HTTPSHandler(BaseHTTPSHandler):
-        def __init__(self, ca_certs, check_domain=True):
-            BaseHTTPSHandler.__init__(self)
-            self.ca_certs = ca_certs
-            self.check_domain = check_domain
-
-        def _conn_maker(self, *args, **kwargs):
-            """
-            This is called to create a connection instance. Normally you'd
-            pass a connection class to do_open, but it doesn't actually check for
-            a class, and just expects a callable. As long as we behave just as a
-            constructor would have, we should be OK. If it ever changes so that
-            we *must* pass a class, we'll create an UnsafeHTTPSConnection class
-            which just sets check_domain to False in the class definition, and
-            choose which one to pass to do_open.
-            """
-            result = HTTPSConnection(*args, **kwargs)
-            if self.ca_certs:
-                result.ca_certs = self.ca_certs
-                result.check_domain = self.check_domain
-            return result
-
-        def https_open(self, req):
-            try:
-                return self.do_open(self._conn_maker, req)
-            except URLError as e:
-                if 'certificate verify failed' in str(e.reason):
-                    raise CertificateError('Unable to verify server certificate '
-                                           'for %s' % req.host)
-                else:
-                    raise
-
-    #
-    # To prevent against mixing HTTP traffic with HTTPS (examples: A Man-In-The-
-    # Middle proxy using HTTP listens on port 443, or an index mistakenly serves
-    # HTML containing a http://xyz link when it should be https://xyz),
-    # you can use the following handler class, which does not allow HTTP traffic.
-    #
-    # It works by inheriting from HTTPHandler - so build_opener won't add a
-    # handler for HTTP itself.
-    #
-    class HTTPSOnlyHandler(HTTPSHandler, HTTPHandler):
-        def http_open(self, req):
-            raise URLError('Unexpected HTTP request on what should be a secure '
-                           'connection: %s' % req)
-
-#
-# XML-RPC with timeouts
-#
-class Transport(xmlrpclib.Transport):
-    def __init__(self, timeout, use_datetime=0):
-        self.timeout = timeout
-        xmlrpclib.Transport.__init__(self, use_datetime)
-
-    def make_connection(self, host):
-        h, eh, x509 = self.get_host_info(host)
-        if not self._connection or host != self._connection[0]:
-            self._extra_headers = eh
-            self._connection = host, httplib.HTTPConnection(h)
-        return self._connection[1]
-
-if ssl:
-    class SafeTransport(xmlrpclib.SafeTransport):
-        def __init__(self, timeout, use_datetime=0):
-            self.timeout = timeout
-            xmlrpclib.SafeTransport.__init__(self, use_datetime)
-
-        def make_connection(self, host):
-            h, eh, kwargs = self.get_host_info(host)
-            if not kwargs:
-                kwargs = {}
-            kwargs['timeout'] = self.timeout
-            if not self._connection or host != self._connection[0]:
-                self._extra_headers = eh
-                self._connection = host, httplib.HTTPSConnection(h, None,
-                                                                 **kwargs)
-            return self._connection[1]
-
-
-class ServerProxy(xmlrpclib.ServerProxy):
-    def __init__(self, uri, **kwargs):
-        self.timeout = timeout = kwargs.pop('timeout', None)
-        # The above classes only come into play if a timeout
-        # is specified
-        if timeout is not None:
-            # scheme = splittype(uri)  # deprecated as of Python 3.8
-            scheme = urlparse(uri)[0]
-            use_datetime = kwargs.get('use_datetime', 0)
-            if scheme == 'https':
-                tcls = SafeTransport
-            else:
-                tcls = Transport
-            kwargs['transport'] = t = tcls(timeout, use_datetime=use_datetime)
-            self.transport = t
-        xmlrpclib.ServerProxy.__init__(self, uri, **kwargs)
-
-#
-# CSV functionality. This is provided because on 2.x, the csv module can't
-# handle Unicode. However, we need to deal with Unicode in e.g. RECORD files.
-#
-
-def _csv_open(fn, mode, **kwargs):
-    if sys.version_info[0] < 3:
-        mode += 'b'
-    else:
-        kwargs['newline'] = ''
-        # Python 3 determines encoding from locale. Force 'utf-8'
-        # file encoding to match other forced utf-8 encoding
-        kwargs['encoding'] = 'utf-8'
-    return open(fn, mode, **kwargs)
-
-
-class CSVBase(object):
-    defaults = {
-        'delimiter': str(','),      # The strs are used because we need native
-        'quotechar': str('"'),      # str in the csv API (2.x won't take
-        'lineterminator': str('\n') # Unicode)
-    }
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *exc_info):
-        self.stream.close()
-
-
-class CSVReader(CSVBase):
-    def __init__(self, **kwargs):
-        if 'stream' in kwargs:
-            stream = kwargs['stream']
-            if sys.version_info[0] >= 3:
-                # needs to be a text stream
-                stream = codecs.getreader('utf-8')(stream)
-            self.stream = stream
-        else:
-            self.stream = _csv_open(kwargs['path'], 'r')
-        self.reader = csv.reader(self.stream, **self.defaults)
-
-    def __iter__(self):
-        return self
-
-    def next(self):
-        result = next(self.reader)
-        if sys.version_info[0] < 3:
-            for i, item in enumerate(result):
-                if not isinstance(item, text_type):
-                    result[i] = item.decode('utf-8')
-        return result
-
-    __next__ = next
-
-class CSVWriter(CSVBase):
-    def __init__(self, fn, **kwargs):
-        self.stream = _csv_open(fn, 'w')
-        self.writer = csv.writer(self.stream, **self.defaults)
-
-    def writerow(self, row):
-        if sys.version_info[0] < 3:
-            r = []
-            for item in row:
-                if isinstance(item, text_type):
-                    item = item.encode('utf-8')
-                r.append(item)
-            row = r
-        self.writer.writerow(row)
-
-#
-#   Configurator functionality
-#
-
-class Configurator(BaseConfigurator):
-
-    value_converters = dict(BaseConfigurator.value_converters)
-    value_converters['inc'] = 'inc_convert'
-
-    def __init__(self, config, base=None):
-        super(Configurator, self).__init__(config)
-        self.base = base or os.getcwd()
-
-    def configure_custom(self, config):
-        def convert(o):
-            if isinstance(o, (list, tuple)):
-                result = type(o)([convert(i) for i in o])
-            elif isinstance(o, dict):
-                if '()' in o:
-                    result = self.configure_custom(o)
-                else:
-                    result = {}
-                    for k in o:
-                        result[k] = convert(o[k])
-            else:
-                result = self.convert(o)
-            return result
-
-        c = config.pop('()')
-        if not callable(c):
-            c = self.resolve(c)
-        props = config.pop('.', None)
-        # Check for valid identifiers
-        args = config.pop('[]', ())
-        if args:
-            args = tuple([convert(o) for o in args])
-        items = [(k, convert(config[k])) for k in config if valid_ident(k)]
-        kwargs = dict(items)
-        result = c(*args, **kwargs)
-        if props:
-            for n, v in props.items():
-                setattr(result, n, convert(v))
-        return result
-
-    def __getitem__(self, key):
-        result = self.config[key]
-        if isinstance(result, dict) and '()' in result:
-            self.config[key] = result = self.configure_custom(result)
-        return result
-
-    def inc_convert(self, value):
-        """Default converter for the inc:// protocol."""
-        if not os.path.isabs(value):
-            value = os.path.join(self.base, value)
-        with codecs.open(value, 'r', encoding='utf-8') as f:
-            result = json.load(f)
-        return result
-
-
-class SubprocessMixin(object):
-    """
-    Mixin for running subprocesses and capturing their output
-    """
-    def __init__(self, verbose=False, progress=None):
-        self.verbose = verbose
-        self.progress = progress
-
-    def reader(self, stream, context):
-        """
-        Read lines from a subprocess' output stream and either pass to a progress
-        callable (if specified) or write progress information to sys.stderr.
-        """
-        progress = self.progress
-        verbose = self.verbose
-        while True:
-            s = stream.readline()
-            if not s:
-                break
-            if progress is not None:
-                progress(s, context)
-            else:
-                if not verbose:
-                    sys.stderr.write('.')
-                else:
-                    sys.stderr.write(s.decode('utf-8'))
-                sys.stderr.flush()
-        stream.close()
-
-    def run_command(self, cmd, **kwargs):
-        p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
-                             stderr=subprocess.PIPE, **kwargs)
-        t1 = threading.Thread(target=self.reader, args=(p.stdout, 'stdout'))
-        t1.start()
-        t2 = threading.Thread(target=self.reader, args=(p.stderr, 'stderr'))
-        t2.start()
-        p.wait()
-        t1.join()
-        t2.join()
-        if self.progress is not None:
-            self.progress('done.', 'main')
-        elif self.verbose:
-            sys.stderr.write('done.\n')
-        return p
-
-
-def normalize_name(name):
-    """Normalize a python package name a la PEP 503"""
-    # https://www.python.org/dev/peps/pep-0503/#normalized-names
-    return re.sub('[-_.]+', '-', name).lower()
-
-# def _get_pypirc_command():
-    # """
-    # Get the distutils command for interacting with PyPI configurations.
-    # :return: the command.
-    # """
-    # from distutils.core import Distribution
-    # from distutils.config import PyPIRCCommand
-    # d = Distribution()
-    # return PyPIRCCommand(d)
-
-class PyPIRCFile(object):
-
-    DEFAULT_REPOSITORY = 'https://upload.pypi.org/legacy/'
-    DEFAULT_REALM = 'pypi'
-
-    def __init__(self, fn=None, url=None):
-        if fn is None:
-            fn = os.path.join(os.path.expanduser('~'), '.pypirc')
-        self.filename = fn
-        self.url = url
-
-    def read(self):
-        result = {}
-
-        if os.path.exists(self.filename):
-            repository = self.url or self.DEFAULT_REPOSITORY
-
-            config = configparser.RawConfigParser()
-            config.read(self.filename)
-            sections = config.sections()
-            if 'distutils' in sections:
-                # let's get the list of servers
-                index_servers = config.get('distutils', 'index-servers')
-                _servers = [server.strip() for server in
-                            index_servers.split('\n')
-                            if server.strip() != '']
-                if _servers == []:
-                    # nothing set, let's try to get the default pypi
-                    if 'pypi' in sections:
-                        _servers = ['pypi']
-                else:
-                    for server in _servers:
-                        result = {'server': server}
-                        result['username'] = config.get(server, 'username')
-
-                        # optional params
-                        for key, default in (('repository', self.DEFAULT_REPOSITORY),
-                                             ('realm', self.DEFAULT_REALM),
-                                             ('password', None)):
-                            if config.has_option(server, key):
-                                result[key] = config.get(server, key)
-                            else:
-                                result[key] = default
-
-                        # work around people having "repository" for the "pypi"
-                        # section of their config set to the HTTP (rather than
-                        # HTTPS) URL
-                        if (server == 'pypi' and
-                            repository in (self.DEFAULT_REPOSITORY, 'pypi')):
-                            result['repository'] = self.DEFAULT_REPOSITORY
-                        elif (result['server'] != repository and
-                              result['repository'] != repository):
-                            result = {}
-            elif 'server-login' in sections:
-                # old format
-                server = 'server-login'
-                if config.has_option(server, 'repository'):
-                    repository = config.get(server, 'repository')
-                else:
-                    repository = self.DEFAULT_REPOSITORY
-                result = {
-                    'username': config.get(server, 'username'),
-                    'password': config.get(server, 'password'),
-                    'repository': repository,
-                    'server': server,
-                    'realm': self.DEFAULT_REALM
-                }
-        return result
-
-    def update(self, username, password):
-        # import pdb; pdb.set_trace()
-        config = configparser.RawConfigParser()
-        fn = self.filename
-        config.read(fn)
-        if not config.has_section('pypi'):
-            config.add_section('pypi')
-        config.set('pypi', 'username', username)
-        config.set('pypi', 'password', password)
-        with open(fn, 'w') as f:
-            config.write(f)
-
-def _load_pypirc(index):
-    """
-    Read the PyPI access configuration as supported by distutils.
-    """
-    return PyPIRCFile(url=index.url).read()
-
-def _store_pypirc(index):
-    PyPIRCFile().update(index.username, index.password)
-
-#
-# get_platform()/get_host_platform() copied from Python 3.10.a0 source, with some minor
-# tweaks
-#
-
-def get_host_platform():
-    """Return a string that identifies the current platform.  This is used mainly to
-    distinguish platform-specific build directories and platform-specific built
-    distributions.  Typically includes the OS name and version and the
-    architecture (as supplied by 'os.uname()'), although the exact information
-    included depends on the OS; eg. on Linux, the kernel version isn't
-    particularly important.
-
-    Examples of returned values:
-       linux-i586
-       linux-alpha (?)
-       solaris-2.6-sun4u
-
-    Windows will return one of:
-       win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc)
-       win32 (all others - specifically, sys.platform is returned)
-
-    For other non-POSIX platforms, currently just returns 'sys.platform'.
-
-    """
-    if os.name == 'nt':
-        if 'amd64' in sys.version.lower():
-            return 'win-amd64'
-        if '(arm)' in sys.version.lower():
-            return 'win-arm32'
-        if '(arm64)' in sys.version.lower():
-            return 'win-arm64'
-        return sys.platform
-
-    # Set for cross builds explicitly
-    if "_PYTHON_HOST_PLATFORM" in os.environ:
-        return os.environ["_PYTHON_HOST_PLATFORM"]
-
-    if os.name != 'posix' or not hasattr(os, 'uname'):
-        # XXX what about the architecture? NT is Intel or Alpha,
-        # Mac OS is M68k or PPC, etc.
-        return sys.platform
-
-    # Try to distinguish various flavours of Unix
-
-    (osname, host, release, version, machine) = os.uname()
-
-    # Convert the OS name to lowercase, remove '/' characters, and translate
-    # spaces (for "Power Macintosh")
-    osname = osname.lower().replace('/', '')
-    machine = machine.replace(' ', '_').replace('/', '-')
-
-    if osname[:5] == 'linux':
-        # At least on Linux/Intel, 'machine' is the processor --
-        # i386, etc.
-        # XXX what about Alpha, SPARC, etc?
-        return  "%s-%s" % (osname, machine)
-
-    elif osname[:5] == 'sunos':
-        if release[0] >= '5':           # SunOS 5 == Solaris 2
-            osname = 'solaris'
-            release = '%d.%s' % (int(release[0]) - 3, release[2:])
-            # We can't use 'platform.architecture()[0]' because a
-            # bootstrap problem. We use a dict to get an error
-            # if some suspicious happens.
-            bitness = {2147483647:'32bit', 9223372036854775807:'64bit'}
-            machine += '.%s' % bitness[sys.maxsize]
-        # fall through to standard osname-release-machine representation
-    elif osname[:3] == 'aix':
-        from _aix_support import aix_platform
-        return aix_platform()
-    elif osname[:6] == 'cygwin':
-        osname = 'cygwin'
-        rel_re = re.compile (r'[\d.]+', re.ASCII)
-        m = rel_re.match(release)
-        if m:
-            release = m.group()
-    elif osname[:6] == 'darwin':
-        import _osx_support, distutils.sysconfig
-        osname, release, machine = _osx_support.get_platform_osx(
-                                        distutils.sysconfig.get_config_vars(),
-                                        osname, release, machine)
-
-    return '%s-%s-%s' % (osname, release, machine)
-
-
-_TARGET_TO_PLAT = {
-    'x86' : 'win32',
-    'x64' : 'win-amd64',
-    'arm' : 'win-arm32',
-}
-
-
-def get_platform():
-    if os.name != 'nt':
-        return get_host_platform()
-    cross_compilation_target = os.environ.get('VSCMD_ARG_TGT_ARCH')
-    if cross_compilation_target not in _TARGET_TO_PLAT:
-        return get_host_platform()
-    return _TARGET_TO_PLAT[cross_compilation_target]

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/live.py

@@ -1,375 +0,0 @@
-import sys
-from threading import Event, RLock, Thread
-from types import TracebackType
-from typing import IO, Any, Callable, List, Optional, TextIO, Type, cast
-
-from . import get_console
-from .console import Console, ConsoleRenderable, RenderableType, RenderHook
-from .control import Control
-from .file_proxy import FileProxy
-from .jupyter import JupyterMixin
-from .live_render import LiveRender, VerticalOverflowMethod
-from .screen import Screen
-from .text import Text
-
-
-class _RefreshThread(Thread):
-    """A thread that calls refresh() at regular intervals."""
-
-    def __init__(self, live: "Live", refresh_per_second: float) -> None:
-        self.live = live
-        self.refresh_per_second = refresh_per_second
-        self.done = Event()
-        super().__init__(daemon=True)
-
-    def stop(self) -> None:
-        self.done.set()
-
-    def run(self) -> None:
-        while not self.done.wait(1 / self.refresh_per_second):
-            with self.live._lock:
-                if not self.done.is_set():
-                    self.live.refresh()
-
-
-class Live(JupyterMixin, RenderHook):
-    """Renders an auto-updating live display of any given renderable.
-
-    Args:
-        renderable (RenderableType, optional): The renderable to live display. Defaults to displaying nothing.
-        console (Console, optional): Optional Console instance. Default will an internal Console instance writing to stdout.
-        screen (bool, optional): Enable alternate screen mode. Defaults to False.
-        auto_refresh (bool, optional): Enable auto refresh. If disabled, you will need to call `refresh()` or `update()` with refresh flag. Defaults to True
-        refresh_per_second (float, optional): Number of times per second to refresh the live display. Defaults to 4.
-        transient (bool, optional): Clear the renderable on exit (has no effect when screen=True). Defaults to False.
-        redirect_stdout (bool, optional): Enable redirection of stdout, so ``print`` may be used. Defaults to True.
-        redirect_stderr (bool, optional): Enable redirection of stderr. Defaults to True.
-        vertical_overflow (VerticalOverflowMethod, optional): How to handle renderable when it is too tall for the console. Defaults to "ellipsis".
-        get_renderable (Callable[[], RenderableType], optional): Optional callable to get renderable. Defaults to None.
-    """
-
-    def __init__(
-        self,
-        renderable: Optional[RenderableType] = None,
-        *,
-        console: Optional[Console] = None,
-        screen: bool = False,
-        auto_refresh: bool = True,
-        refresh_per_second: float = 4,
-        transient: bool = False,
-        redirect_stdout: bool = True,
-        redirect_stderr: bool = True,
-        vertical_overflow: VerticalOverflowMethod = "ellipsis",
-        get_renderable: Optional[Callable[[], RenderableType]] = None,
-    ) -> None:
-        assert refresh_per_second > 0, "refresh_per_second must be > 0"
-        self._renderable = renderable
-        self.console = console if console is not None else get_console()
-        self._screen = screen
-        self._alt_screen = False
-
-        self._redirect_stdout = redirect_stdout
-        self._redirect_stderr = redirect_stderr
-        self._restore_stdout: Optional[IO[str]] = None
-        self._restore_stderr: Optional[IO[str]] = None
-
-        self._lock = RLock()
-        self.ipy_widget: Optional[Any] = None
-        self.auto_refresh = auto_refresh
-        self._started: bool = False
-        self.transient = True if screen else transient
-
-        self._refresh_thread: Optional[_RefreshThread] = None
-        self.refresh_per_second = refresh_per_second
-
-        self.vertical_overflow = vertical_overflow
-        self._get_renderable = get_renderable
-        self._live_render = LiveRender(
-            self.get_renderable(), vertical_overflow=vertical_overflow
-        )
-
-    @property
-    def is_started(self) -> bool:
-        """Check if live display has been started."""
-        return self._started
-
-    def get_renderable(self) -> RenderableType:
-        renderable = (
-            self._get_renderable()
-            if self._get_renderable is not None
-            else self._renderable
-        )
-        return renderable or ""
-
-    def start(self, refresh: bool = False) -> None:
-        """Start live rendering display.
-
-        Args:
-            refresh (bool, optional): Also refresh. Defaults to False.
-        """
-        with self._lock:
-            if self._started:
-                return
-            self.console.set_live(self)
-            self._started = True
-            if self._screen:
-                self._alt_screen = self.console.set_alt_screen(True)
-            self.console.show_cursor(False)
-            self._enable_redirect_io()
-            self.console.push_render_hook(self)
-            if refresh:
-                try:
-                    self.refresh()
-                except Exception:
-                    # If refresh fails, we want to stop the redirection of sys.stderr,
-                    # so the error stacktrace is properly displayed in the terminal.
-                    # (or, if the code that calls Rich captures the exception and wants to display something,
-                    # let this be displayed in the terminal).
-                    self.stop()
-                    raise
-            if self.auto_refresh:
-                self._refresh_thread = _RefreshThread(self, self.refresh_per_second)
-                self._refresh_thread.start()
-
-    def stop(self) -> None:
-        """Stop live rendering display."""
-        with self._lock:
-            if not self._started:
-                return
-            self.console.clear_live()
-            self._started = False
-
-            if self.auto_refresh and self._refresh_thread is not None:
-                self._refresh_thread.stop()
-                self._refresh_thread = None
-            # allow it to fully render on the last even if overflow
-            self.vertical_overflow = "visible"
-            with self.console:
-                try:
-                    if not self._alt_screen and not self.console.is_jupyter:
-                        self.refresh()
-                finally:
-                    self._disable_redirect_io()
-                    self.console.pop_render_hook()
-                    if not self._alt_screen and self.console.is_terminal:
-                        self.console.line()
-                    self.console.show_cursor(True)
-                    if self._alt_screen:
-                        self.console.set_alt_screen(False)
-
-                    if self.transient and not self._alt_screen:
-                        self.console.control(self._live_render.restore_cursor())
-                    if self.ipy_widget is not None and self.transient:
-                        self.ipy_widget.close()  # pragma: no cover
-
-    def __enter__(self) -> "Live":
-        self.start(refresh=self._renderable is not None)
-        return self
-
-    def __exit__(
-        self,
-        exc_type: Optional[Type[BaseException]],
-        exc_val: Optional[BaseException],
-        exc_tb: Optional[TracebackType],
-    ) -> None:
-        self.stop()
-
-    def _enable_redirect_io(self) -> None:
-        """Enable redirecting of stdout / stderr."""
-        if self.console.is_terminal or self.console.is_jupyter:
-            if self._redirect_stdout and not isinstance(sys.stdout, FileProxy):
-                self._restore_stdout = sys.stdout
-                sys.stdout = cast("TextIO", FileProxy(self.console, sys.stdout))
-            if self._redirect_stderr and not isinstance(sys.stderr, FileProxy):
-                self._restore_stderr = sys.stderr
-                sys.stderr = cast("TextIO", FileProxy(self.console, sys.stderr))
-
-    def _disable_redirect_io(self) -> None:
-        """Disable redirecting of stdout / stderr."""
-        if self._restore_stdout:
-            sys.stdout = cast("TextIO", self._restore_stdout)
-            self._restore_stdout = None
-        if self._restore_stderr:
-            sys.stderr = cast("TextIO", self._restore_stderr)
-            self._restore_stderr = None
-
-    @property
-    def renderable(self) -> RenderableType:
-        """Get the renderable that is being displayed
-
-        Returns:
-            RenderableType: Displayed renderable.
-        """
-        renderable = self.get_renderable()
-        return Screen(renderable) if self._alt_screen else renderable
-
-    def update(self, renderable: RenderableType, *, refresh: bool = False) -> None:
-        """Update the renderable that is being displayed
-
-        Args:
-            renderable (RenderableType): New renderable to use.
-            refresh (bool, optional): Refresh the display. Defaults to False.
-        """
-        if isinstance(renderable, str):
-            renderable = self.console.render_str(renderable)
-        with self._lock:
-            self._renderable = renderable
-            if refresh:
-                self.refresh()
-
-    def refresh(self) -> None:
-        """Update the display of the Live Render."""
-        with self._lock:
-            self._live_render.set_renderable(self.renderable)
-            if self.console.is_jupyter:  # pragma: no cover
-                try:
-                    from IPython.display import display
-                    from ipywidgets import Output
-                except ImportError:
-                    import warnings
-
-                    warnings.warn('install "ipywidgets" for Jupyter support')
-                else:
-                    if self.ipy_widget is None:
-                        self.ipy_widget = Output()
-                        display(self.ipy_widget)
-
-                    with self.ipy_widget:
-                        self.ipy_widget.clear_output(wait=True)
-                        self.console.print(self._live_render.renderable)
-            elif self.console.is_terminal and not self.console.is_dumb_terminal:
-                with self.console:
-                    self.console.print(Control())
-            elif (
-                not self._started and not self.transient
-            ):  # if it is finished allow files or dumb-terminals to see final result
-                with self.console:
-                    self.console.print(Control())
-
-    def process_renderables(
-        self, renderables: List[ConsoleRenderable]
-    ) -> List[ConsoleRenderable]:
-        """Process renderables to restore cursor and display progress."""
-        self._live_render.vertical_overflow = self.vertical_overflow
-        if self.console.is_interactive:
-            # lock needs acquiring as user can modify live_render renderable at any time unlike in Progress.
-            with self._lock:
-                reset = (
-                    Control.home()
-                    if self._alt_screen
-                    else self._live_render.position_cursor()
-                )
-                renderables = [reset, *renderables, self._live_render]
-        elif (
-            not self._started and not self.transient
-        ):  # if it is finished render the final output for files or dumb_terminals
-            renderables = [*renderables, self._live_render]
-
-        return renderables
-
-
-if __name__ == "__main__":  # pragma: no cover
-    import random
-    import time
-    from itertools import cycle
-    from typing import Dict, List, Tuple
-
-    from .align import Align
-    from .console import Console
-    from .live import Live as Live
-    from .panel import Panel
-    from .rule import Rule
-    from .syntax import Syntax
-    from .table import Table
-
-    console = Console()
-
-    syntax = Syntax(
-        '''def loop_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]:
-    """Iterate and generate a tuple with a flag for last value."""
-    iter_values = iter(values)
-    try:
-        previous_value = next(iter_values)
-    except StopIteration:
-        return
-    for value in iter_values:
-        yield False, previous_value
-        previous_value = value
-    yield True, previous_value''',
-        "python",
-        line_numbers=True,
-    )
-
-    table = Table("foo", "bar", "baz")
-    table.add_row("1", "2", "3")
-
-    progress_renderables = [
-        "You can make the terminal shorter and taller to see the live table hide"
-        "Text may be printed while the progress bars are rendering.",
-        Panel("In fact, [i]any[/i] renderable will work"),
-        "Such as [magenta]tables[/]...",
-        table,
-        "Pretty printed structures...",
-        {"type": "example", "text": "Pretty printed"},
-        "Syntax...",
-        syntax,
-        Rule("Give it a try!"),
-    ]
-
-    examples = cycle(progress_renderables)
-
-    exchanges = [
-        "SGD",
-        "MYR",
-        "EUR",
-        "USD",
-        "AUD",
-        "JPY",
-        "CNH",
-        "HKD",
-        "CAD",
-        "INR",
-        "DKK",
-        "GBP",
-        "RUB",
-        "NZD",
-        "MXN",
-        "IDR",
-        "TWD",
-        "THB",
-        "VND",
-    ]
-    with Live(console=console) as live_table:
-        exchange_rate_dict: Dict[Tuple[str, str], float] = {}
-
-        for index in range(100):
-            select_exchange = exchanges[index % len(exchanges)]
-
-            for exchange in exchanges:
-                if exchange == select_exchange:
-                    continue
-                time.sleep(0.4)
-                if random.randint(0, 10) < 1:
-                    console.log(next(examples))
-                exchange_rate_dict[(select_exchange, exchange)] = 200 / (
-                    (random.random() * 320) + 1
-                )
-                if len(exchange_rate_dict) > len(exchanges) - 1:
-                    exchange_rate_dict.pop(list(exchange_rate_dict.keys())[0])
-                table = Table(title="Exchange Rates")
-
-                table.add_column("Source Currency")
-                table.add_column("Destination Currency")
-                table.add_column("Exchange Rate")
-
-                for ((source, dest), exchange_rate) in exchange_rate_dict.items():
-                    table.add_row(
-                        source,
-                        dest,
-                        Text(
-                            f"{exchange_rate:.4f}",
-                            style="red" if exchange_rate < 1.0 else "green",
-                        ),
-                    )
-
-                live_table.update(Align.center(table))

spaces/Awesimo/jojogan/e4e/models/stylegan2/op/upfirdn2d.py

@@ -1,184 +0,0 @@
-import os
-
-import torch
-from torch.autograd import Function
-from torch.utils.cpp_extension import load
-
-module_path = os.path.dirname(__file__)
-upfirdn2d_op = load(
-    'upfirdn2d',
-    sources=[
-        os.path.join(module_path, 'upfirdn2d.cpp'),
-        os.path.join(module_path, 'upfirdn2d_kernel.cu'),
-    ],
-)
-
-
-class UpFirDn2dBackward(Function):
-    @staticmethod
-    def forward(
-            ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
-    ):
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_op.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        kernel, = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_op.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(
-            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
-        )
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        batch, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_op.upfirdn2d(
-            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-        )
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    out = UpFirDn2d.apply(
-        input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
-    )
-
-    return out
-
-
-def upfirdn2d_native(
-        input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-          :,
-          max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0),
-          max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0),
-          :,
-          ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-
-    return out[:, ::down_y, ::down_x, :]

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/c10.py

@@ -1,534 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import math
-import torch
-import torch.nn.functional as F
-
-from detectron2.layers import cat
-from detectron2.layers.roi_align_rotated import ROIAlignRotated
-from detectron2.modeling import poolers
-from detectron2.modeling.proposal_generator import rpn
-from detectron2.modeling.roi_heads.mask_head import mask_rcnn_inference
-from detectron2.structures import Boxes, ImageList, Instances, Keypoints
-
-from .shared import alias, to_device
-
-
-"""
-This file contains caffe2-compatible implementation of several detectron2 components.
-"""
-
-
-class Caffe2Boxes(Boxes):
-    """
-    Representing a list of detectron2.structures.Boxes from minibatch, each box
-    is represented by a 5d vector (batch index + 4 coordinates), or a 6d vector
-    (batch index + 5 coordinates) for RotatedBoxes.
-    """
-
-    def __init__(self, tensor):
-        assert isinstance(tensor, torch.Tensor)
-        assert tensor.dim() == 2 and tensor.size(-1) in [4, 5, 6], tensor.size()
-        # TODO: make tensor immutable when dim is Nx5 for Boxes,
-        # and Nx6 for RotatedBoxes?
-        self.tensor = tensor
-
-
-# TODO clean up this class, maybe just extend Instances
-class InstancesList(object):
-    """
-    Tensor representation of a list of Instances object for a batch of images.
-
-    When dealing with a batch of images with Caffe2 ops, a list of bboxes
-    (instances) are usually represented by single Tensor with size
-    (sigma(Ni), 5) or (sigma(Ni), 4) plus a batch split Tensor. This class is
-    for providing common functions to convert between these two representations.
-    """
-
-    def __init__(self, im_info, indices, extra_fields=None):
-        # [N, 3] -> (H, W, Scale)
-        self.im_info = im_info
-        # [N,] -> indice of batch to which the instance belongs
-        self.indices = indices
-        # [N, ...]
-        self.batch_extra_fields = extra_fields or {}
-
-        self.image_size = self.im_info
-
-    def get_fields(self):
-        """like `get_fields` in the Instances object,
-        but return each field in tensor representations"""
-        ret = {}
-        for k, v in self.batch_extra_fields.items():
-            # if isinstance(v, torch.Tensor):
-            #     tensor_rep = v
-            # elif isinstance(v, (Boxes, Keypoints)):
-            #     tensor_rep = v.tensor
-            # else:
-            #     raise ValueError("Can't find tensor representation for: {}".format())
-            ret[k] = v
-        return ret
-
-    def has(self, name):
-        return name in self.batch_extra_fields
-
-    def set(self, name, value):
-        data_len = len(value)
-        if len(self.batch_extra_fields):
-            assert (
-                len(self) == data_len
-            ), "Adding a field of length {} to a Instances of length {}".format(data_len, len(self))
-        self.batch_extra_fields[name] = value
-
-    def __setattr__(self, name, val):
-        if name in ["im_info", "indices", "batch_extra_fields", "image_size"]:
-            super().__setattr__(name, val)
-        else:
-            self.set(name, val)
-
-    def __getattr__(self, name):
-        if name not in self.batch_extra_fields:
-            raise AttributeError("Cannot find field '{}' in the given Instances!".format(name))
-        return self.batch_extra_fields[name]
-
-    def __len__(self):
-        return len(self.indices)
-
-    def flatten(self):
-        ret = []
-        for _, v in self.batch_extra_fields.items():
-            if isinstance(v, (Boxes, Keypoints)):
-                ret.append(v.tensor)
-            else:
-                ret.append(v)
-        return ret
-
-    @staticmethod
-    def to_d2_instances_list(instances_list):
-        """
-        Convert InstancesList to List[Instances]. The input `instances_list` can
-        also be a List[Instances], in this case this method is a non-op.
-        """
-        if not isinstance(instances_list, InstancesList):
-            assert all(isinstance(x, Instances) for x in instances_list)
-            return instances_list
-
-        ret = []
-        for i, info in enumerate(instances_list.im_info):
-            instances = Instances(torch.Size([int(info[0].item()), int(info[1].item())]))
-
-            ids = instances_list.indices == i
-            for k, v in instances_list.batch_extra_fields.items():
-                if isinstance(v, torch.Tensor):
-                    instances.set(k, v[ids])
-                    continue
-                elif isinstance(v, Boxes):
-                    instances.set(k, v[ids, -4:])
-                    continue
-
-                target_type, tensor_source = v
-                assert isinstance(tensor_source, torch.Tensor)
-                assert tensor_source.shape[0] == instances_list.indices.shape[0]
-                tensor_source = tensor_source[ids]
-
-                if issubclass(target_type, Boxes):
-                    instances.set(k, Boxes(tensor_source[:, -4:]))
-                elif issubclass(target_type, Keypoints):
-                    instances.set(k, Keypoints(tensor_source))
-                elif issubclass(target_type, torch.Tensor):
-                    instances.set(k, tensor_source)
-                else:
-                    raise ValueError("Can't handle targe type: {}".format(target_type))
-
-            ret.append(instances)
-        return ret
-
-
-class Caffe2Compatible(object):
-    """
-    A model can inherit this class to indicate that it can be traced and deployed with caffe2.
-    """
-
-    def _get_tensor_mode(self):
-        return self._tensor_mode
-
-    def _set_tensor_mode(self, v):
-        self._tensor_mode = v
-
-    tensor_mode = property(_get_tensor_mode, _set_tensor_mode)
-    """
-    If true, the model expects C2-style tensor only inputs/outputs format.
-    """
-
-
-class Caffe2RPN(Caffe2Compatible, rpn.RPN):
-    def _generate_proposals(
-        self, images, objectness_logits_pred, anchor_deltas_pred, gt_instances=None
-    ):
-        assert isinstance(images, ImageList)
-        if self.tensor_mode:
-            im_info = images.image_sizes
-        else:
-            im_info = torch.tensor([[im_sz[0], im_sz[1], 1.0] for im_sz in images.image_sizes]).to(
-                images.tensor.device
-            )
-        assert isinstance(im_info, torch.Tensor)
-
-        rpn_rois_list = []
-        rpn_roi_probs_list = []
-        for scores, bbox_deltas, cell_anchors_tensor, feat_stride in zip(
-            objectness_logits_pred,
-            anchor_deltas_pred,
-            iter(self.anchor_generator.cell_anchors),
-            self.anchor_generator.strides,
-        ):
-            scores = scores.detach()
-            bbox_deltas = bbox_deltas.detach()
-
-            rpn_rois, rpn_roi_probs = torch.ops._caffe2.GenerateProposals(
-                scores,
-                bbox_deltas,
-                im_info,
-                cell_anchors_tensor,
-                spatial_scale=1.0 / feat_stride,
-                pre_nms_topN=self.pre_nms_topk[self.training],
-                post_nms_topN=self.post_nms_topk[self.training],
-                nms_thresh=self.nms_thresh,
-                min_size=self.min_box_size,
-                # correct_transform_coords=True,  # deprecated argument
-                angle_bound_on=True,  # Default
-                angle_bound_lo=-180,
-                angle_bound_hi=180,
-                clip_angle_thresh=1.0,  # Default
-                legacy_plus_one=False,
-            )
-            rpn_rois_list.append(rpn_rois)
-            rpn_roi_probs_list.append(rpn_roi_probs)
-
-        # For FPN in D2, in RPN all proposals from different levels are concated
-        # together, ranked and picked by top post_nms_topk. Then in ROIPooler
-        # it calculates level_assignments and calls the RoIAlign from
-        # the corresponding level.
-
-        if len(objectness_logits_pred) == 1:
-            rpn_rois = rpn_rois_list[0]
-            rpn_roi_probs = rpn_roi_probs_list[0]
-        else:
-            assert len(rpn_rois_list) == len(rpn_roi_probs_list)
-            rpn_post_nms_topN = self.post_nms_topk[self.training]
-
-            device = rpn_rois_list[0].device
-            input_list = [to_device(x, "cpu") for x in (rpn_rois_list + rpn_roi_probs_list)]
-
-            # TODO remove this after confirming rpn_max_level/rpn_min_level
-            # is not needed in CollectRpnProposals.
-            feature_strides = list(self.anchor_generator.strides)
-            rpn_min_level = int(math.log2(feature_strides[0]))
-            rpn_max_level = int(math.log2(feature_strides[-1]))
-            assert (rpn_max_level - rpn_min_level + 1) == len(
-                rpn_rois_list
-            ), "CollectRpnProposals requires continuous levels"
-
-            rpn_rois = torch.ops._caffe2.CollectRpnProposals(
-                input_list,
-                # NOTE: in current implementation, rpn_max_level and rpn_min_level
-                # are not needed, only the subtraction of two matters and it
-                # can be infer from the number of inputs. Keep them now for
-                # consistency.
-                rpn_max_level=2 + len(rpn_rois_list) - 1,
-                rpn_min_level=2,
-                rpn_post_nms_topN=rpn_post_nms_topN,
-            )
-            rpn_rois = to_device(rpn_rois, device)
-            rpn_roi_probs = []
-
-        proposals = self.c2_postprocess(im_info, rpn_rois, rpn_roi_probs, self.tensor_mode)
-        return proposals, {}
-
-    def forward(self, images, features, gt_instances=None):
-        assert not self.training
-        features = [features[f] for f in self.in_features]
-        objectness_logits_pred, anchor_deltas_pred = self.rpn_head(features)
-        return self._generate_proposals(
-            images,
-            objectness_logits_pred,
-            anchor_deltas_pred,
-            gt_instances,
-        )
-
-    @staticmethod
-    def c2_postprocess(im_info, rpn_rois, rpn_roi_probs, tensor_mode):
-        proposals = InstancesList(
-            im_info=im_info,
-            indices=rpn_rois[:, 0],
-            extra_fields={
-                "proposal_boxes": Caffe2Boxes(rpn_rois),
-                "objectness_logits": (torch.Tensor, rpn_roi_probs),
-            },
-        )
-        if not tensor_mode:
-            proposals = InstancesList.to_d2_instances_list(proposals)
-        else:
-            proposals = [proposals]
-        return proposals
-
-
-class Caffe2ROIPooler(Caffe2Compatible, poolers.ROIPooler):
-    @staticmethod
-    def c2_preprocess(box_lists):
-        assert all(isinstance(x, Boxes) for x in box_lists)
-        if all(isinstance(x, Caffe2Boxes) for x in box_lists):
-            # input is pure-tensor based
-            assert len(box_lists) == 1
-            pooler_fmt_boxes = box_lists[0].tensor
-        else:
-            pooler_fmt_boxes = poolers.convert_boxes_to_pooler_format(box_lists)
-        return pooler_fmt_boxes
-
-    def forward(self, x, box_lists):
-        assert not self.training
-
-        pooler_fmt_boxes = self.c2_preprocess(box_lists)
-        num_level_assignments = len(self.level_poolers)
-
-        if num_level_assignments == 1:
-            if isinstance(self.level_poolers[0], ROIAlignRotated):
-                c2_roi_align = torch.ops._caffe2.RoIAlignRotated
-                aligned = True
-            else:
-                c2_roi_align = torch.ops._caffe2.RoIAlign
-                aligned = self.level_poolers[0].aligned
-
-            x0 = x[0]
-            if x0.is_quantized:
-                x0 = x0.dequantize()
-
-            out = c2_roi_align(
-                x0,
-                pooler_fmt_boxes,
-                order="NCHW",
-                spatial_scale=float(self.level_poolers[0].spatial_scale),
-                pooled_h=int(self.output_size[0]),
-                pooled_w=int(self.output_size[1]),
-                sampling_ratio=int(self.level_poolers[0].sampling_ratio),
-                aligned=aligned,
-            )
-            return out
-
-        device = pooler_fmt_boxes.device
-        assert (
-            self.max_level - self.min_level + 1 == 4
-        ), "Currently DistributeFpnProposals only support 4 levels"
-        fpn_outputs = torch.ops._caffe2.DistributeFpnProposals(
-            to_device(pooler_fmt_boxes, "cpu"),
-            roi_canonical_scale=self.canonical_box_size,
-            roi_canonical_level=self.canonical_level,
-            roi_max_level=self.max_level,
-            roi_min_level=self.min_level,
-            legacy_plus_one=False,
-        )
-        fpn_outputs = [to_device(x, device) for x in fpn_outputs]
-
-        rois_fpn_list = fpn_outputs[:-1]
-        rois_idx_restore_int32 = fpn_outputs[-1]
-
-        roi_feat_fpn_list = []
-        for roi_fpn, x_level, pooler in zip(rois_fpn_list, x, self.level_poolers):
-            if isinstance(pooler, ROIAlignRotated):
-                c2_roi_align = torch.ops._caffe2.RoIAlignRotated
-                aligned = True
-            else:
-                c2_roi_align = torch.ops._caffe2.RoIAlign
-                aligned = bool(pooler.aligned)
-
-            if x_level.is_quantized:
-                x_level = x_level.dequantize()
-
-            roi_feat_fpn = c2_roi_align(
-                x_level,
-                roi_fpn,
-                order="NCHW",
-                spatial_scale=float(pooler.spatial_scale),
-                pooled_h=int(self.output_size[0]),
-                pooled_w=int(self.output_size[1]),
-                sampling_ratio=int(pooler.sampling_ratio),
-                aligned=aligned,
-            )
-            roi_feat_fpn_list.append(roi_feat_fpn)
-
-        roi_feat_shuffled = cat(roi_feat_fpn_list, dim=0)
-        assert roi_feat_shuffled.numel() > 0 and rois_idx_restore_int32.numel() > 0, (
-            "Caffe2 export requires tracing with a model checkpoint + input that can produce valid"
-            " detections. But no detections were obtained with the given checkpoint and input!"
-        )
-        roi_feat = torch.ops._caffe2.BatchPermutation(roi_feat_shuffled, rois_idx_restore_int32)
-        return roi_feat
-
-
-class Caffe2FastRCNNOutputsInference:
-    def __init__(self, tensor_mode):
-        self.tensor_mode = tensor_mode  # whether the output is caffe2 tensor mode
-
-    def __call__(self, box_predictor, predictions, proposals):
-        """equivalent to FastRCNNOutputLayers.inference"""
-        num_classes = box_predictor.num_classes
-        score_thresh = box_predictor.test_score_thresh
-        nms_thresh = box_predictor.test_nms_thresh
-        topk_per_image = box_predictor.test_topk_per_image
-        is_rotated = len(box_predictor.box2box_transform.weights) == 5
-
-        if is_rotated:
-            box_dim = 5
-            assert box_predictor.box2box_transform.weights[4] == 1, (
-                "The weights for Rotated BBoxTransform in C2 have only 4 dimensions,"
-                + " thus enforcing the angle weight to be 1 for now"
-            )
-            box2box_transform_weights = box_predictor.box2box_transform.weights[:4]
-        else:
-            box_dim = 4
-            box2box_transform_weights = box_predictor.box2box_transform.weights
-
-        class_logits, box_regression = predictions
-        if num_classes + 1 == class_logits.shape[1]:
-            class_prob = F.softmax(class_logits, -1)
-        else:
-            assert num_classes == class_logits.shape[1]
-            class_prob = F.sigmoid(class_logits)
-            # BoxWithNMSLimit will infer num_classes from the shape of the class_prob
-            # So append a zero column as placeholder for the background class
-            class_prob = torch.cat((class_prob, torch.zeros(class_prob.shape[0], 1)), dim=1)
-
-        assert box_regression.shape[1] % box_dim == 0
-        cls_agnostic_bbox_reg = box_regression.shape[1] // box_dim == 1
-
-        input_tensor_mode = proposals[0].proposal_boxes.tensor.shape[1] == box_dim + 1
-
-        rois = type(proposals[0].proposal_boxes).cat([p.proposal_boxes for p in proposals])
-        device, dtype = rois.tensor.device, rois.tensor.dtype
-        if input_tensor_mode:
-            im_info = proposals[0].image_size
-            rois = rois.tensor
-        else:
-            im_info = torch.tensor(
-                [[sz[0], sz[1], 1.0] for sz in [x.image_size for x in proposals]]
-            )
-            batch_ids = cat(
-                [
-                    torch.full((b, 1), i, dtype=dtype, device=device)
-                    for i, b in enumerate(len(p) for p in proposals)
-                ],
-                dim=0,
-            )
-            rois = torch.cat([batch_ids, rois.tensor], dim=1)
-
-        roi_pred_bbox, roi_batch_splits = torch.ops._caffe2.BBoxTransform(
-            to_device(rois, "cpu"),
-            to_device(box_regression, "cpu"),
-            to_device(im_info, "cpu"),
-            weights=box2box_transform_weights,
-            apply_scale=True,
-            rotated=is_rotated,
-            angle_bound_on=True,
-            angle_bound_lo=-180,
-            angle_bound_hi=180,
-            clip_angle_thresh=1.0,
-            legacy_plus_one=False,
-        )
-        roi_pred_bbox = to_device(roi_pred_bbox, device)
-        roi_batch_splits = to_device(roi_batch_splits, device)
-
-        nms_outputs = torch.ops._caffe2.BoxWithNMSLimit(
-            to_device(class_prob, "cpu"),
-            to_device(roi_pred_bbox, "cpu"),
-            to_device(roi_batch_splits, "cpu"),
-            score_thresh=float(score_thresh),
-            nms=float(nms_thresh),
-            detections_per_im=int(topk_per_image),
-            soft_nms_enabled=False,
-            soft_nms_method="linear",
-            soft_nms_sigma=0.5,
-            soft_nms_min_score_thres=0.001,
-            rotated=is_rotated,
-            cls_agnostic_bbox_reg=cls_agnostic_bbox_reg,
-            input_boxes_include_bg_cls=False,
-            output_classes_include_bg_cls=False,
-            legacy_plus_one=False,
-        )
-        roi_score_nms = to_device(nms_outputs[0], device)
-        roi_bbox_nms = to_device(nms_outputs[1], device)
-        roi_class_nms = to_device(nms_outputs[2], device)
-        roi_batch_splits_nms = to_device(nms_outputs[3], device)
-        roi_keeps_nms = to_device(nms_outputs[4], device)
-        roi_keeps_size_nms = to_device(nms_outputs[5], device)
-        if not self.tensor_mode:
-            roi_class_nms = roi_class_nms.to(torch.int64)
-
-        roi_batch_ids = cat(
-            [
-                torch.full((b, 1), i, dtype=dtype, device=device)
-                for i, b in enumerate(int(x.item()) for x in roi_batch_splits_nms)
-            ],
-            dim=0,
-        )
-
-        roi_class_nms = alias(roi_class_nms, "class_nms")
-        roi_score_nms = alias(roi_score_nms, "score_nms")
-        roi_bbox_nms = alias(roi_bbox_nms, "bbox_nms")
-        roi_batch_splits_nms = alias(roi_batch_splits_nms, "batch_splits_nms")
-        roi_keeps_nms = alias(roi_keeps_nms, "keeps_nms")
-        roi_keeps_size_nms = alias(roi_keeps_size_nms, "keeps_size_nms")
-
-        results = InstancesList(
-            im_info=im_info,
-            indices=roi_batch_ids[:, 0],
-            extra_fields={
-                "pred_boxes": Caffe2Boxes(roi_bbox_nms),
-                "scores": roi_score_nms,
-                "pred_classes": roi_class_nms,
-            },
-        )
-
-        if not self.tensor_mode:
-            results = InstancesList.to_d2_instances_list(results)
-            batch_splits = roi_batch_splits_nms.int().tolist()
-            kept_indices = list(roi_keeps_nms.to(torch.int64).split(batch_splits))
-        else:
-            results = [results]
-            kept_indices = [roi_keeps_nms]
-
-        return results, kept_indices
-
-
-class Caffe2MaskRCNNInference:
-    def __call__(self, pred_mask_logits, pred_instances):
-        """equivalent to mask_head.mask_rcnn_inference"""
-        if all(isinstance(x, InstancesList) for x in pred_instances):
-            assert len(pred_instances) == 1
-            mask_probs_pred = pred_mask_logits.sigmoid()
-            mask_probs_pred = alias(mask_probs_pred, "mask_fcn_probs")
-            pred_instances[0].pred_masks = mask_probs_pred
-        else:
-            mask_rcnn_inference(pred_mask_logits, pred_instances)
-
-
-class Caffe2KeypointRCNNInference:
-    def __init__(self, use_heatmap_max_keypoint):
-        self.use_heatmap_max_keypoint = use_heatmap_max_keypoint
-
-    def __call__(self, pred_keypoint_logits, pred_instances):
-        # just return the keypoint heatmap for now,
-        # there will be option to call HeatmapMaxKeypointOp
-        output = alias(pred_keypoint_logits, "kps_score")
-        if all(isinstance(x, InstancesList) for x in pred_instances):
-            assert len(pred_instances) == 1
-            if self.use_heatmap_max_keypoint:
-                device = output.device
-                output = torch.ops._caffe2.HeatmapMaxKeypoint(
-                    to_device(output, "cpu"),
-                    pred_instances[0].pred_boxes.tensor,
-                    should_output_softmax=True,  # worth make it configerable?
-                )
-                output = to_device(output, device)
-                output = alias(output, "keypoints_out")
-            pred_instances[0].pred_keypoints = output
-        return pred_keypoint_logits

spaces/BAAI/AltDiffusion/README.md

@@ -1,13 +0,0 @@
----
-title: AltDiffusion
-emoji: ❤️
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 3.10.1
-app_file: app.py
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Benson/text-generation/Examples/20 Minutos Hasta El Amanecer Descarga Gratuita.md

@@ -1,61 +0,0 @@
-<br />
-<h1>20 minutos hasta el amanecer: un juego de supervivencia Roguelite revisión</h1>
-<p>Si usted está buscando un ritmo rápido, lleno de acción, y desafiante juego que pondrá a prueba sus habilidades y reflejos, entonces es posible que desee echa un vistazo 20 Minutes Till Dawn. Este es un juego de supervivencia roguelite donde tienes que luchar contra hordas interminables de monstruos Lovecraftian y sobrevivir a la noche. En este artículo, revisaremos las características del juego, la jugabilidad, los gráficos, el sonido, los pros, los contras y más. </p>
- <h2>Introducción</h2>
-<p>20 Minutes Till Dawn es un videojuego roguelike shoot 'em up desarrollado y publicado por flanne. El juego fue lanzado en acceso temprano en Steam el 8 de junio de 2022, y fue portado a Android e iOS por Erabit Studios el 9 de septiembre, 2022. El juego salió de Steam con la versión 1.0 el 8 de junio de 2023.</p>
-<h2>20 minutos hasta el amanecer descarga gratuita</h2><br /><p><b><b>Download</b> --->>> <a href="https://bltlly.com/2v6MGV">https://bltlly.com/2v6MGV</a></b></p><br /><br />
-<p>El juego pertenece al género de la supervivencia roguelite, lo que significa que cuenta con permadeath, aleatorización y progresión a través de carreras. El objetivo del juego es sobrevivir durante 20 minutos hasta el amanecer, mientras se enfrenta a un ataque de monstruos que se vuelven más fuertes y más numerosos a medida que pasa el tiempo. El juego está inspirado en Vampire Survivors, pero con opciones de combate y personalización más activas. </p>
-<p>El juego está disponible en Steam por $4.99, así como en Google Play, App Store y TapTap gratis. El juego ha recibido críticas muy positivas de jugadores y críticos por igual, con más de 20.000 comentarios en Steam y más de 6 millones de descargas en plataformas móviles. El juego también ha sido presentado por IGN, TheGamer, Level Winner y otros medios de comunicación. </p>
- <h2>Juego</h2>
-<p>El modo de juego de 20 Minutes Till Dawn es simple pero desafiante. Usted controla a un personaje que puede moverse con las teclas WASD o un joystick virtual, apuntar con el ratón o la pantalla táctil, y disparar con clic izquierdo o toque. También puedes usar el botón derecho o doble toque para activar tu habilidad especial, que varía dependiendo de tu personaje. </p>
-
-<p>A medida que matas monstruos, ganas puntos de experiencia que te permiten subir de nivel. Cada vez que subes de nivel, puedes elegir una de las cuatro mejoras generadas al azar que mejoran tus estadísticas o habilidades. Estas mejoras pueden ir desde aumentar tu daño o salud, hasta agregar efectos como fuego, veneno o aturdimiento a tus ataques, hasta desbloquear nuevas habilidades como guion, escudo o invocación. Las actualizaciones son permanentes para la ejecución actual, pero se pierden cuando mueres o reinicias. </p>
-<p>Para sobrevivir a la noche, tienes que seguir moviéndote y disparando, evitando los ataques de los enemigos y los peligros ambientales. Los enemigos vienen en diferentes formas y tamaños, cada uno con su propio comportamiento y patrón de ataque. Algunos de ellos son rápidos y ágiles, algunos son lentos y sucios, algunos son a distancia y explosivos, y algunos son sigilosos y mortales. También encontrarás jefes cada pocos minutos, que son mucho más fuertes y más duros que los enemigos normales. Los jefes tienen habilidades y debilidades únicas que tienes que explotar para derrotarlos. </p>
-<p>El juego tiene cuatro modos de juego diferentes: Normal, Hardcore, Endless y Custom. El modo normal es el modo predeterminado, donde tienes que sobrevivir durante 20 minutos con tres vidas. El modo Hardcore es similar al modo Normal, pero solo tienes una vida y los enemigos son más agresivos. El modo sin fin es donde puedes jugar todo el tiempo que quieras, pero los enemigos se vuelven más difíciles y más frecuentes a medida que pasa el tiempo. El modo personalizado es donde puedes crear tus propias reglas y ajustes para el juego, como cambiar el límite de tiempo, la tasa de aparición de enemigos, el nivel de dificultad y más. </p>
- <h2>Gráficos y sonido</h2>
-
-<p>El sonido de 20 Minutes Till Dawn es envolvente y cautivador, con una banda sonora que coincide con el estado de ánimo y la intensidad del juego. El juego tiene una música estilo synthwave que es pegadiza y energética, con diferentes pistas para cada entorno y jefe. El juego también tiene efectos de sonido que son realistas y satisfactorios, como el sonido de disparos, explosiones, gritos, pasos y más. El juego no tiene voz ni diálogo, pero tiene mensajes de texto que aparecen en la pantalla para darte pistas o advertencias. </p>
-<p></p>
-<p>El juego funciona bien en la mayoría de los dispositivos y plataformas, con un juego suave y un retraso mínimo o problemas técnicos. El juego tiene bajos requisitos del sistema para los usuarios de PC, así como opciones para ajustar la calidad de los gráficos y la resolución para los usuarios móviles. El juego también es compatible con el ahorro de la nube , soporte de controlador, tablas de clasificación , logros , y cooperativo multijugador . </p>
- <h2>Pros y contras</h2>
-<p>20 Minutes Till Dawn es un juego divertido y adictivo que te mantendrá entretenido durante horas. Sin embargo, como cualquier otro juego, también tiene sus pros y sus contras. Aquí están algunos de ellos:</p>
-<tabla>
-<tr><th>Pros</th><th>Contras</th></tr>
-<tr><td>- Juego rápido y desafiante que requiere habilidad y estrategia</td><td>- Permadeath puede ser frustrante y desalentador para algunos jugadores</td></tr>
-<tr><td>- Variedad de personajes, armas, mejoras, enemigos, jefes, entornos y modos de juego que ofrecen valor de reproducción</td><td>- La aleatorización puede ser injusta o desequilibrada a veces</td></tr>
-<tr><td>- Gráficos de estilo retro que son coloridos y atmosféricos</td><td>- Los gráficos pixelados pueden no atraer a todos</td></tr>
-<tr><td>- Música estilo synthwave que es pegadiza y energética</td><td>- La música puede ser repetitiva o molesta después de un rato</td></tr>
-<tr><td>- Bajos requisitos del sistema y compatibilidad multiplataforma</td><td>- Algunos errores o fallos ocasionales pueden ocurrir</td></tr>
-</tabla>
- <h2>Conclusión</h2>
-
-<p>Si estás interesado en jugar 20 Minutes Till Dawn, puedes encontrar más información o descargar el juego desde los siguientes enlaces:</p>
-<ul>
-<li>Vapor: [20 minutos hasta el amanecer en el vapor]</li>
-<li>Google Play: [20 minutos hasta el amanecer - Aplicaciones en Google Play]</li>
-<li>App Store: [ 20 minutos hasta el amanecer en la App Store]</li>
-<li>TapTap: [20 minutos hasta el amanecer - TapTap]</li>
-</ul>
-<p>También puede ver algunos videos de juego o leer algunos comentarios de las siguientes fuentes:</p>
-<ul>
-<li>IGN: [20 minutos hasta el amanecer Revisión - IGN]</li>
-<li>TheGamer: [20 minutos hasta el amanecer Revisión: Una Roguelite que te mantiene en sus dedos de los pies]</li>
-<li>Nivel ganador: [20 minutos hasta el amanecer Guía para principiantes: Consejos, trucos y estrategias para sobrevivir la noche]</li>
-</ul>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas más frecuentes sobre 20 minutos hasta el amanecer:</p>
-<ol>
-<li><b>¿Cómo puedo desbloquear más personajes y armas? </b></li>
-<p>Puedes desbloquear más personajes y armas gastando gemas, que se ganan matando monstruos o completando logros. También puedes encontrar algunas armas como botín gotas de enemigos o cofres. </p>
-<li><b>¿Cómo puedo guardar mi progreso? </b></li>
-<p>Puede guardar su progreso utilizando la función de almacenamiento en la nube, que está disponible en todas las plataformas. También puede utilizar la función de ahorro local, que está disponible en PC y plataformas móviles. Sin embargo, tenga en cuenta que su progreso solo se guarda entre ejecuciones, no durante las ejecuciones. Si muere o reinicia, perderá sus actualizaciones y elementos actuales. </p>
-<li><b>¿Cómo puedo jugar con mis amigos? </b></li>
-<p>Puedes jugar con tus amigos usando la función multijugador co-op, que está disponible en todas las plataformas. Puedes unirte o alojar un juego con hasta cuatro jugadores en línea o localmente. También puedes chatear con tus amigos usando la función de chat de voz o texto. </p>
-<li><b>¿Cómo cambio la configuración del juego? </b></li>
-
-<li><b>¿Cómo puedo contactar a los desarrolladores o reportar un error? </b></li>
-<p>Puede ponerse en contacto con los desarrolladores o informar de un error mediante la función de retroalimentación, que está disponible en todas las plataformas. También puede visitar el sitio web oficial, el servidor de discordia, la página de Twitter o la página de Facebook del juego. </p>
-</ol>
- <p>Espero que hayas disfrutado de este artículo y te haya resultado útil. Si tienes alguna pregunta o comentario, puedes dejarlos abajo. Gracias por leer y tener un gran día! </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Billyosoro/ESRGAN/realesrgan/__init__.py

@@ -1,6 +0,0 @@
-# flake8: noqa
-from .archs import *
-from .data import *
-from .models import *
-from .utils import *
-#from .version import *

spaces/Bradjan310/ehartford-Wizard-Vicuna-30B-Uncensored/app.py

@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/ehartford/Wizard-Vicuna-30B-Uncensored").launch()

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/.github/ISSUE_TEMPLATE/questions-help-support.md

@@ -1,21 +0,0 @@
----
-name: "❓How to do something?"
-about: How to do X with detectron2? How detectron2 does X?
-
----
-
-## ❓ How to use Detectron2
-
-Questions like:
-
-1. How to do X with detectron2?
-2. How detectron2 does X?
-
-NOTE:
-
-1. If you met any unexpected issue when using detectron2 and wish to know why,
-   please use the "Unexpected Problems / Bugs" issue template.
-
-2. We do not answer general machine learning / computer vision questions that are not specific to
-	 detectron2, such as how a model works, how to improve your training/make it converge, or what algorithm/methods can be
-	 used to achieve X.

spaces/CVPR/LIVE/thrust/thrust/detail/temporary_buffer.h

@@ -1,76 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/detail/execution_policy.h>
-#include <thrust/pair.h>
-#include <thrust/detail/pointer.h>
-#include <thrust/detail/raw_pointer_cast.h>
-#include <thrust/detail/execute_with_allocator.h>
-#include <thrust/system/detail/generic/temporary_buffer.h>
-#include <thrust/system/detail/adl/temporary_buffer.h>
-
-namespace thrust
-{
-namespace detail
-{
-
-
-template<typename T, typename DerivedPolicy, typename Pair>
-__host__ __device__
-  thrust::pair<thrust::pointer<T,DerivedPolicy>, typename thrust::pointer<T,DerivedPolicy>::difference_type>
-    down_cast_pair(Pair p)
-{
-  // XXX should use a hypothetical thrust::static_pointer_cast here
-  thrust::pointer<T,DerivedPolicy> ptr = thrust::pointer<T,DerivedPolicy>(static_cast<T*>(thrust::raw_pointer_cast(p.first)));
-
-  typedef thrust::pair<thrust::pointer<T,DerivedPolicy>, typename thrust::pointer<T,DerivedPolicy>::difference_type> result_type;
-  return result_type(ptr, p.second);
-} // end down_cast_pair()
-
-
-} // end detail
-
-
-__thrust_exec_check_disable__
-template<typename T, typename DerivedPolicy>
-__host__ __device__
-  thrust::pair<thrust::pointer<T,DerivedPolicy>, typename thrust::pointer<T,DerivedPolicy>::difference_type>
-    get_temporary_buffer(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, typename thrust::pointer<T,DerivedPolicy>::difference_type n)
-{
-  using thrust::detail::get_temporary_buffer; // execute_with_allocator
-  using thrust::system::detail::generic::get_temporary_buffer;
-
-  return thrust::detail::down_cast_pair<T,DerivedPolicy>(get_temporary_buffer<T>(thrust::detail::derived_cast(thrust::detail::strip_const(exec)), n));
-} // end get_temporary_buffer()
-
-
-__thrust_exec_check_disable__
-template<typename DerivedPolicy, typename Pointer>
-__host__ __device__
-  void return_temporary_buffer(const thrust::detail::execution_policy_base<DerivedPolicy> &exec, Pointer p, std::ptrdiff_t n)
-{
-  using thrust::detail::return_temporary_buffer; // execute_with_allocator
-  using thrust::system::detail::generic::return_temporary_buffer;
-
-  return return_temporary_buffer(thrust::detail::derived_cast(thrust::detail::strip_const(exec)), p, n);
-} // end return_temporary_buffer()
-
-
-} // end thrust
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/generate.h

@@ -1,57 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-template<typename ExecutionPolicy,
-         typename ForwardIterator,
-         typename Generator>
-__host__ __device__
-  void generate(thrust::execution_policy<ExecutionPolicy> &exec,
-                ForwardIterator first,
-                ForwardIterator last,
-                Generator gen);
-
-template<typename ExecutionPolicy,
-         typename OutputIterator,
-         typename Size,
-         typename Generator>
-__host__ __device__
-  OutputIterator generate_n(thrust::execution_policy<ExecutionPolicy> &exec,
-                            OutputIterator first,
-                            Size n,
-                            Generator gen);
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/generate.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/scatter.h

@@ -1,81 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <thrust/system/detail/generic/tag.h>
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename RandomAccessIterator>
-__host__ __device__
-  void scatter(thrust::execution_policy<DerivedPolicy> &exec,
-               InputIterator1 first,
-               InputIterator1 last,
-               InputIterator2 map,
-               RandomAccessIterator output);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename InputIterator3,
-         typename RandomAccessIterator>
-__host__ __device__
-  void scatter_if(thrust::execution_policy<DerivedPolicy> &exec,
-                  InputIterator1 first,
-                  InputIterator1 last,
-                  InputIterator2 map,
-                  InputIterator3 stencil,
-                  RandomAccessIterator output);
-
-
-template<typename DerivedPolicy,
-         typename InputIterator1,
-         typename InputIterator2,
-         typename InputIterator3,
-         typename RandomAccessIterator,
-         typename Predicate>
-__host__ __device__
-  void scatter_if(thrust::execution_policy<DerivedPolicy> &exec,
-                  InputIterator1 first,
-                  InputIterator1 last,
-                  InputIterator2 map,
-                  InputIterator3 stencil,
-                  RandomAccessIterator output,
-                  Predicate pred);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include <thrust/system/detail/generic/scatter.inl>
-

spaces/CVPR/WALT/walt/datasets/pipelines/transforms.py

@@ -1,1861 +0,0 @@
-import copy
-import inspect
-
-import mmcv
-import numpy as np
-from numpy import random
-
-from mmdet.core import PolygonMasks
-from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
-from ..builder import PIPELINES
-
-try:
-    from imagecorruptions import corrupt
-except ImportError:
-    corrupt = None
-
-try:
-    import albumentations
-    from albumentations import Compose
-except ImportError:
-    albumentations = None
-    Compose = None
-
-
-@PIPELINES.register_module()
-class Resize(object):
-    """Resize images & bbox & mask.
-
-    This transform resizes the input image to some scale. Bboxes and masks are
-    then resized with the same scale factor. If the input dict contains the key
-    "scale", then the scale in the input dict is used, otherwise the specified
-    scale in the init method is used. If the input dict contains the key
-    "scale_factor" (if MultiScaleFlipAug does not give img_scale but
-    scale_factor), the actual scale will be computed by image shape and
-    scale_factor.
-
-    `img_scale` can either be a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 3 multiscale modes:
-
-    - ``ratio_range is not None``: randomly sample a ratio from the ratio \
-      range and multiply it with the image scale.
-    - ``ratio_range is None`` and ``multiscale_mode == "range"``: randomly \
-      sample a scale from the multiscale range.
-    - ``ratio_range is None`` and ``multiscale_mode == "value"``: randomly \
-      sample a scale from multiple scales.
-
-    Args:
-        img_scale (tuple or list[tuple]): Images scales for resizing.
-        multiscale_mode (str): Either "range" or "value".
-        ratio_range (tuple[float]): (min_ratio, max_ratio)
-        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
-            image.
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-        backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
-            These two backends generates slightly different results. Defaults
-            to 'cv2'.
-        override (bool, optional): Whether to override `scale` and
-            `scale_factor` so as to call resize twice. Default False. If True,
-            after the first resizing, the existed `scale` and `scale_factor`
-            will be ignored so the second resizing can be allowed.
-            This option is a work-around for multiple times of resize in DETR.
-            Defaults to False.
-    """
-
-    def __init__(self,
-                 img_scale=None,
-                 multiscale_mode='range',
-                 ratio_range=None,
-                 keep_ratio=True,
-                 bbox_clip_border=True,
-                 backend='cv2',
-                 override=False):
-        if img_scale is None:
-            self.img_scale = None
-        else:
-            if isinstance(img_scale, list):
-                self.img_scale = img_scale
-            else:
-                self.img_scale = [img_scale]
-            assert mmcv.is_list_of(self.img_scale, tuple)
-
-        if ratio_range is not None:
-            # mode 1: given a scale and a range of image ratio
-            assert len(self.img_scale) == 1
-        else:
-            # mode 2: given multiple scales or a range of scales
-            assert multiscale_mode in ['value', 'range']
-
-        self.backend = backend
-        self.multiscale_mode = multiscale_mode
-        self.ratio_range = ratio_range
-        self.keep_ratio = keep_ratio
-        # TODO: refactor the override option in Resize
-        self.override = override
-        self.bbox_clip_border = bbox_clip_border
-
-    @staticmethod
-    def random_select(img_scales):
-        """Randomly select an img_scale from given candidates.
-
-        Args:
-            img_scales (list[tuple]): Images scales for selection.
-
-        Returns:
-            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, \
-                where ``img_scale`` is the selected image scale and \
-                ``scale_idx`` is the selected index in the given candidates.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple)
-        scale_idx = np.random.randint(len(img_scales))
-        img_scale = img_scales[scale_idx]
-        return img_scale, scale_idx
-
-    @staticmethod
-    def random_sample(img_scales):
-        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
-
-        Args:
-            img_scales (list[tuple]): Images scale range for sampling.
-                There must be two tuples in img_scales, which specify the lower
-                and upper bound of image scales.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(img_scale, None)``, where \
-                ``img_scale`` is sampled scale and None is just a placeholder \
-                to be consistent with :func:`random_select`.
-        """
-
-        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
-        img_scale_long = [max(s) for s in img_scales]
-        img_scale_short = [min(s) for s in img_scales]
-        long_edge = np.random.randint(
-            min(img_scale_long),
-            max(img_scale_long) + 1)
-        short_edge = np.random.randint(
-            min(img_scale_short),
-            max(img_scale_short) + 1)
-        img_scale = (long_edge, short_edge)
-        return img_scale, None
-
-    @staticmethod
-    def random_sample_ratio(img_scale, ratio_range):
-        """Randomly sample an img_scale when ``ratio_range`` is specified.
-
-        A ratio will be randomly sampled from the range specified by
-        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
-        generate sampled scale.
-
-        Args:
-            img_scale (tuple): Images scale base to multiply with ratio.
-            ratio_range (tuple[float]): The minimum and maximum ratio to scale
-                the ``img_scale``.
-
-        Returns:
-            (tuple, None): Returns a tuple ``(scale, None)``, where \
-                ``scale`` is sampled ratio multiplied with ``img_scale`` and \
-                None is just a placeholder to be consistent with \
-                :func:`random_select`.
-        """
-
-        assert isinstance(img_scale, tuple) and len(img_scale) == 2
-        min_ratio, max_ratio = ratio_range
-        assert min_ratio <= max_ratio
-        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
-        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
-        return scale, None
-
-    def _random_scale(self, results):
-        """Randomly sample an img_scale according to ``ratio_range`` and
-        ``multiscale_mode``.
-
-        If ``ratio_range`` is specified, a ratio will be sampled and be
-        multiplied with ``img_scale``.
-        If multiple scales are specified by ``img_scale``, a scale will be
-        sampled according to ``multiscale_mode``.
-        Otherwise, single scale will be used.
-
-        Args:
-            results (dict): Result dict from :obj:`dataset`.
-
-        Returns:
-            dict: Two new keys 'scale` and 'scale_idx` are added into \
-                ``results``, which would be used by subsequent pipelines.
-        """
-
-        if self.ratio_range is not None:
-            scale, scale_idx = self.random_sample_ratio(
-                self.img_scale[0], self.ratio_range)
-        elif len(self.img_scale) == 1:
-            scale, scale_idx = self.img_scale[0], 0
-        elif self.multiscale_mode == 'range':
-            scale, scale_idx = self.random_sample(self.img_scale)
-        elif self.multiscale_mode == 'value':
-            scale, scale_idx = self.random_select(self.img_scale)
-        else:
-            raise NotImplementedError
-
-        results['scale'] = scale
-        results['scale_idx'] = scale_idx
-
-    def _resize_img(self, results):
-        """Resize images with ``results['scale']``."""
-        for key in results.get('img_fields', ['img']):
-            if self.keep_ratio:
-                img, scale_factor = mmcv.imrescale(
-                    results[key],
-                    results['scale'],
-                    return_scale=True,
-                    backend=self.backend)
-                # the w_scale and h_scale has minor difference
-                # a real fix should be done in the mmcv.imrescale in the future
-                new_h, new_w = img.shape[:2]
-                h, w = results[key].shape[:2]
-                w_scale = new_w / w
-                h_scale = new_h / h
-            else:
-                img, w_scale, h_scale = mmcv.imresize(
-                    results[key],
-                    results['scale'],
-                    return_scale=True,
-                    backend=self.backend)
-            results[key] = img
-
-            scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
-                                    dtype=np.float32)
-            results['img_shape'] = img.shape
-            # in case that there is no padding
-            results['pad_shape'] = img.shape
-            results['scale_factor'] = scale_factor
-            results['keep_ratio'] = self.keep_ratio
-
-    def _resize_bboxes(self, results):
-        """Resize bounding boxes with ``results['scale_factor']``."""
-        for key in results.get('bbox_fields', []):
-            bboxes = results[key] * results['scale_factor']
-            if self.bbox_clip_border:
-                img_shape = results['img_shape']
-                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
-                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
-            results[key] = bboxes
-
-    def _resize_bboxes3d(self, results):
-        """Resize bounding boxes with ``results['scale_factor']``."""
-        key = 'gt_bboxes_3d_proj'
-        bboxes3d_proj = results[key][:,:,:2]
-        img_shape = results['img_shape']
-        for i in range(results[key].shape[1]):
-            bboxes3d_proj[:,i,:] = bboxes3d_proj[:,i,:] * results['scale_factor'][:2]
-            if self.bbox_clip_border:
-                bboxes3d_proj[:, i, 0] = np.clip(bboxes3d_proj[:, i, 0], 0, img_shape[1])
-                bboxes3d_proj[:, i, 1] = np.clip(bboxes3d_proj[:, i, 1], 0, img_shape[1])
-        results[key] = bboxes3d_proj
-
-    def _resize_masks(self, results):
-        """Resize masks with ``results['scale']``"""
-        for key in results.get('mask_fields', []):
-            if results[key] is None:
-                continue
-            if self.keep_ratio:
-                results[key] = results[key].rescale(results['scale'])
-            else:
-                results[key] = results[key].resize(results['img_shape'][:2])
-
-    def _resize_seg(self, results):
-        """Resize semantic segmentation map with ``results['scale']``."""
-        for key in results.get('seg_fields', []):
-            if self.keep_ratio:
-                gt_seg = mmcv.imrescale(
-                    results[key],
-                    results['scale'],
-                    interpolation='nearest',
-                    backend=self.backend)
-            else:
-                gt_seg = mmcv.imresize(
-                    results[key],
-                    results['scale'],
-                    interpolation='nearest',
-                    backend=self.backend)
-            results['gt_semantic_seg'] = gt_seg
-
-    def __call__(self, results):
-        """Call function to resize images, bounding boxes, masks, semantic
-        segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', \
-                'keep_ratio' keys are added into result dict.
-        """
-
-        if 'scale' not in results:
-            if 'scale_factor' in results:
-                img_shape = results['img'].shape[:2]
-                scale_factor = results['scale_factor']
-                assert isinstance(scale_factor, float)
-                results['scale'] = tuple(
-                    [int(x * scale_factor) for x in img_shape][::-1])
-            else:
-                self._random_scale(results)
-        else:
-            if not self.override:
-                assert 'scale_factor' not in results, (
-                    'scale and scale_factor cannot be both set.')
-            else:
-                results.pop('scale')
-                if 'scale_factor' in results:
-                    results.pop('scale_factor')
-                self._random_scale(results)
-
-        self._resize_img(results)
-        self._resize_bboxes(results)
-        self._resize_bboxes3d(results)
-        self._resize_masks(results)
-        self._resize_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(img_scale={self.img_scale}, '
-        repr_str += f'multiscale_mode={self.multiscale_mode}, '
-        repr_str += f'ratio_range={self.ratio_range}, '
-        repr_str += f'keep_ratio={self.keep_ratio}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomFlip(object):
-    """Flip the image & bbox & mask.
-
-    If the input dict contains the key "flip", then the flag will be used,
-    otherwise it will be randomly decided by a ratio specified in the init
-    method.
-
-    When random flip is enabled, ``flip_ratio``/``direction`` can either be a
-    float/string or tuple of float/string. There are 3 flip modes:
-
-    - ``flip_ratio`` is float, ``direction`` is string: the image will be
-        ``direction``ly flipped with probability of ``flip_ratio`` .
-        E.g., ``flip_ratio=0.5``, ``direction='horizontal'``,
-        then image will be horizontally flipped with probability of 0.5.
-    - ``flip_ratio`` is float, ``direction`` is list of string: the image wil
-        be ``direction[i]``ly flipped with probability of
-        ``flip_ratio/len(direction)``.
-        E.g., ``flip_ratio=0.5``, ``direction=['horizontal', 'vertical']``,
-        then image will be horizontally flipped with probability of 0.25,
-        vertically with probability of 0.25.
-    - ``flip_ratio`` is list of float, ``direction`` is list of string:
-        given ``len(flip_ratio) == len(direction)``, the image wil
-        be ``direction[i]``ly flipped with probability of ``flip_ratio[i]``.
-        E.g., ``flip_ratio=[0.3, 0.5]``, ``direction=['horizontal',
-        'vertical']``, then image will be horizontally flipped with probability
-         of 0.3, vertically with probability of 0.5
-
-    Args:
-        flip_ratio (float | list[float], optional): The flipping probability.
-            Default: None.
-        direction(str | list[str], optional): The flipping direction. Options
-            are 'horizontal', 'vertical', 'diagonal'. Default: 'horizontal'.
-            If input is a list, the length must equal ``flip_ratio``. Each
-            element in ``flip_ratio`` indicates the flip probability of
-            corresponding direction.
-    """
-
-    def __init__(self, flip_ratio=None, direction='horizontal'):
-        if isinstance(flip_ratio, list):
-            assert mmcv.is_list_of(flip_ratio, float)
-            assert 0 <= sum(flip_ratio) <= 1
-        elif isinstance(flip_ratio, float):
-            assert 0 <= flip_ratio <= 1
-        elif flip_ratio is None:
-            pass
-        else:
-            raise ValueError('flip_ratios must be None, float, '
-                             'or list of float')
-        self.flip_ratio = flip_ratio
-
-        valid_directions = ['horizontal', 'vertical', 'diagonal']
-        if isinstance(direction, str):
-            assert direction in valid_directions
-        elif isinstance(direction, list):
-            assert mmcv.is_list_of(direction, str)
-            assert set(direction).issubset(set(valid_directions))
-        else:
-            raise ValueError('direction must be either str or list of str')
-        self.direction = direction
-
-        if isinstance(flip_ratio, list):
-            assert len(self.flip_ratio) == len(self.direction)
-
-    def bbox_flip(self, bboxes, img_shape, direction):
-        """Flip bboxes horizontally.
-
-        Args:
-            bboxes (numpy.ndarray): Bounding boxes, shape (..., 4*k)
-            img_shape (tuple[int]): Image shape (height, width)
-            direction (str): Flip direction. Options are 'horizontal',
-                'vertical'.
-
-        Returns:
-            numpy.ndarray: Flipped bounding boxes.
-        """
-
-        assert bboxes.shape[-1] % 4 == 0
-        flipped = bboxes.copy()
-        if direction == 'horizontal':
-            w = img_shape[1]
-            flipped[..., 0::4] = w - bboxes[..., 2::4]
-            flipped[..., 2::4] = w - bboxes[..., 0::4]
-        elif direction == 'vertical':
-            h = img_shape[0]
-            flipped[..., 1::4] = h - bboxes[..., 3::4]
-            flipped[..., 3::4] = h - bboxes[..., 1::4]
-        elif direction == 'diagonal':
-            w = img_shape[1]
-            h = img_shape[0]
-            flipped[..., 0::4] = w - bboxes[..., 2::4]
-            flipped[..., 1::4] = h - bboxes[..., 3::4]
-            flipped[..., 2::4] = w - bboxes[..., 0::4]
-            flipped[..., 3::4] = h - bboxes[..., 1::4]
-        else:
-            raise ValueError(f"Invalid flipping direction '{direction}'")
-        return flipped
-
-    def bbox3d_proj_flip(self, bboxes, img_shape, direction):
-        """Flip bboxes horizontally.
-
-        Args:
-            bboxes (numpy.ndarray): Bounding boxes, shape (..., 4*k)
-            img_shape (tuple[int]): Image shape (height, width)
-            direction (str): Flip direction. Options are 'horizontal',
-                'vertical'.
-
-        Returns:
-            numpy.ndarray: Flipped bounding boxes.
-        """
-
-        flipped = bboxes.copy()
-        if direction == 'horizontal':
-            w = img_shape[1]
-            
-            flipped[:,:,0] = w - bboxes[:,:, 0]
-        elif direction == 'vertical':
-            h = img_shape[0]
-            flipped[:,:,1] = h - bboxes[:,:, 1]
-        elif direction == 'diagonal':
-            w = img_shape[1]
-            h = img_shape[0]
-            flipped[:,:,0] = w - bboxes[:,:, 0]
-            flipped[:,:,1] = h - bboxes[:,:, 1]
-        else:
-            raise ValueError(f"Invalid flipping direction '{direction}'")
-        flipped[bboxes == -100] = -100
-        return flipped
-
-
-    def __call__(self, results):
-        """Call function to flip bounding boxes, masks, semantic segmentation
-        maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Flipped results, 'flip', 'flip_direction' keys are added \
-                into result dict.
-        """
-
-        if 'flip' not in results:
-            if isinstance(self.direction, list):
-                # None means non-flip
-                direction_list = self.direction + [None]
-            else:
-                # None means non-flip
-                direction_list = [self.direction, None]
-
-            if isinstance(self.flip_ratio, list):
-                non_flip_ratio = 1 - sum(self.flip_ratio)
-                flip_ratio_list = self.flip_ratio + [non_flip_ratio]
-            else:
-                non_flip_ratio = 1 - self.flip_ratio
-                # exclude non-flip
-                single_ratio = self.flip_ratio / (len(direction_list) - 1)
-                flip_ratio_list = [single_ratio] * (len(direction_list) -
-                                                    1) + [non_flip_ratio]
-
-            cur_dir = np.random.choice(direction_list, p=flip_ratio_list)
-
-            results['flip'] = cur_dir is not None
-        if 'flip_direction' not in results:
-            results['flip_direction'] = cur_dir
-        if results['flip']:
-            # flip image
-            for key in results.get('img_fields', ['img']):
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction'])
-            # flip bboxes
-            for key in results.get('bbox_fields', []):
-                results[key] = self.bbox_flip(results[key],
-                                              results['img_shape'],
-                                              results['flip_direction'])
-            for key in results.get('bbox3d_fields', []):
-                if '_proj' in key:
-                    results[key] = self.bbox3d_proj_flip(results[key],
-                                              results['img_shape'],
-                                              results['flip_direction'])
-            # flip masks
-            for key in results.get('mask_fields', []):
-                results[key] = results[key].flip(results['flip_direction'])
-
-            # flip segs
-            for key in results.get('seg_fields', []):
-                results[key] = mmcv.imflip(
-                    results[key], direction=results['flip_direction'])
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(flip_ratio={self.flip_ratio})'
-
-
-@PIPELINES.register_module()
-class Pad(object):
-    """Pad the image & mask.
-
-    There are two padding modes: (1) pad to a fixed size and (2) pad to the
-    minimum size that is divisible by some number.
-    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
-
-    Args:
-        size (tuple, optional): Fixed padding size.
-        size_divisor (int, optional): The divisor of padded size.
-        pad_val (float, optional): Padding value, 0 by default.
-    """
-
-    def __init__(self, size=None, size_divisor=None, pad_val=0):
-        self.size = size
-        self.size_divisor = size_divisor
-        self.pad_val = pad_val
-        # only one of size and size_divisor should be valid
-        assert size is not None or size_divisor is not None
-        assert size is None or size_divisor is None
-
-    def _pad_img(self, results):
-        """Pad images according to ``self.size``."""
-        for key in results.get('img_fields', ['img']):
-            if self.size is not None:
-                padded_img = mmcv.impad(
-                    results[key], shape=self.size, pad_val=self.pad_val)
-            elif self.size_divisor is not None:
-                padded_img = mmcv.impad_to_multiple(
-                    results[key], self.size_divisor, pad_val=self.pad_val)
-            results[key] = padded_img
-        results['pad_shape'] = padded_img.shape
-        results['pad_fixed_size'] = self.size
-        results['pad_size_divisor'] = self.size_divisor
-
-    def _pad_masks(self, results):
-        """Pad masks according to ``results['pad_shape']``."""
-        pad_shape = results['pad_shape'][:2]
-        for key in results.get('mask_fields', []):
-            results[key] = results[key].pad(pad_shape, pad_val=self.pad_val)
-
-    def _pad_seg(self, results):
-        """Pad semantic segmentation map according to
-        ``results['pad_shape']``."""
-        for key in results.get('seg_fields', []):
-            results[key] = mmcv.impad(
-                results[key], shape=results['pad_shape'][:2])
-
-    def __call__(self, results):
-        """Call function to pad images, masks, semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Updated result dict.
-        """
-        self._pad_img(results)
-        self._pad_masks(results)
-        self._pad_seg(results)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(size={self.size}, '
-        repr_str += f'size_divisor={self.size_divisor}, '
-        repr_str += f'pad_val={self.pad_val})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Normalize(object):
-    """Normalize the image.
-
-    Added key is "img_norm_cfg".
-
-    Args:
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB,
-            default is true.
-    """
-
-    def __init__(self, mean, std, to_rgb=True):
-        self.mean = np.array(mean, dtype=np.float32)
-        self.std = np.array(std, dtype=np.float32)
-        self.to_rgb = to_rgb
-
-    def __call__(self, results):
-        """Call function to normalize images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Normalized results, 'img_norm_cfg' key is added into
-                result dict.
-        """
-        for key in results.get('img_fields', ['img']):
-            results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
-                                            self.to_rgb)
-        results['img_norm_cfg'] = dict(
-            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCrop(object):
-    """Random crop the image & bboxes & masks.
-
-    The absolute `crop_size` is sampled based on `crop_type` and `image_size`,
-    then the cropped results are generated.
-
-    Args:
-        crop_size (tuple): The relative ratio or absolute pixels of
-            height and width.
-        crop_type (str, optional): one of "relative_range", "relative",
-            "absolute", "absolute_range". "relative" randomly crops
-            (h * crop_size[0], w * crop_size[1]) part from an input of size
-            (h, w). "relative_range" uniformly samples relative crop size from
-            range [crop_size[0], 1] and [crop_size[1], 1] for height and width
-            respectively. "absolute" crops from an input with absolute size
-            (crop_size[0], crop_size[1]). "absolute_range" uniformly samples
-            crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w
-            in range [crop_size[0], min(w, crop_size[1])]. Default "absolute".
-        allow_negative_crop (bool, optional): Whether to allow a crop that does
-            not contain any bbox area. Default False.
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-
-    Note:
-        - If the image is smaller than the absolute crop size, return the
-            original image.
-        - The keys for bboxes, labels and masks must be aligned. That is,
-          `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and
-          `gt_bboxes_ignore` corresponds to `gt_labels_ignore` and
-          `gt_masks_ignore`.
-        - If the crop does not contain any gt-bbox region and
-          `allow_negative_crop` is set to False, skip this image.
-    """
-
-    def __init__(self,
-                 crop_size,
-                 crop_type='absolute',
-                 allow_negative_crop=False,
-                 bbox_clip_border=True):
-        if crop_type not in [
-                'relative_range', 'relative', 'absolute', 'absolute_range'
-        ]:
-            raise ValueError(f'Invalid crop_type {crop_type}.')
-        if crop_type in ['absolute', 'absolute_range']:
-            assert crop_size[0] > 0 and crop_size[1] > 0
-            assert isinstance(crop_size[0], int) and isinstance(
-                crop_size[1], int)
-        else:
-            assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1
-        self.crop_size = crop_size
-        self.crop_type = crop_type
-        self.allow_negative_crop = allow_negative_crop
-        self.bbox_clip_border = bbox_clip_border
-        # The key correspondence from bboxes to labels and masks.
-        self.bbox2label = {
-            'gt_bboxes': 'gt_labels',
-            'gt_bboxes_ignore': 'gt_labels_ignore'
-        }
-        self.bbox2mask = {
-            'gt_bboxes': 'gt_masks',
-            'gt_bboxes_ignore': 'gt_masks_ignore'
-        }
-
-    def _crop_data(self, results, crop_size, allow_negative_crop):
-        """Function to randomly crop images, bounding boxes, masks, semantic
-        segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-            crop_size (tuple): Expected absolute size after cropping, (h, w).
-            allow_negative_crop (bool): Whether to allow a crop that does not
-                contain any bbox area. Default to False.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-        assert crop_size[0] > 0 and crop_size[1] > 0
-        for key in results.get('img_fields', ['img']):
-            img = results[key]
-            margin_h = max(img.shape[0] - crop_size[0], 0)
-            margin_w = max(img.shape[1] - crop_size[1], 0)
-            offset_h = np.random.randint(0, margin_h + 1)
-            offset_w = np.random.randint(0, margin_w + 1)
-            crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
-            crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]
-
-            # crop the image
-            img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
-            img_shape = img.shape
-            results[key] = img
-        results['img_shape'] = img_shape
-
-        # crop bboxes accordingly and clip to the image boundary
-        for key in results.get('bbox_fields', []):
-            # e.g. gt_bboxes and gt_bboxes_ignore
-            bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
-                                   dtype=np.float32)
-            bboxes = results[key] - bbox_offset
-            if self.bbox_clip_border:
-                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
-                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
-            valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & (
-                bboxes[:, 3] > bboxes[:, 1])
-            # If the crop does not contain any gt-bbox area and
-            # allow_negative_crop is False, skip this image.
-            if (key == 'gt_bboxes' and not valid_inds.any()
-                    and not allow_negative_crop):
-                return None
-            results[key] = bboxes[valid_inds, :]
-            # label fields. e.g. gt_labels and gt_labels_ignore
-            label_key = self.bbox2label.get(key)
-            if label_key in results:
-                results[label_key] = results[label_key][valid_inds]
-
-            # mask fields, e.g. gt_masks and gt_masks_ignore
-            mask_key = self.bbox2mask.get(key)
-            if mask_key in results:
-                results[mask_key] = results[mask_key][
-                    valid_inds.nonzero()[0]].crop(
-                        np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))
-
-        # crop semantic seg
-        for key in results.get('seg_fields', []):
-            results[key] = results[key][crop_y1:crop_y2, crop_x1:crop_x2]
-
-        return results
-
-    def _get_crop_size(self, image_size):
-        """Randomly generates the absolute crop size based on `crop_type` and
-        `image_size`.
-
-        Args:
-            image_size (tuple): (h, w).
-
-        Returns:
-            crop_size (tuple): (crop_h, crop_w) in absolute pixels.
-        """
-        h, w = image_size
-        if self.crop_type == 'absolute':
-            return (min(self.crop_size[0], h), min(self.crop_size[1], w))
-        elif self.crop_type == 'absolute_range':
-            assert self.crop_size[0] <= self.crop_size[1]
-            crop_h = np.random.randint(
-                min(h, self.crop_size[0]),
-                min(h, self.crop_size[1]) + 1)
-            crop_w = np.random.randint(
-                min(w, self.crop_size[0]),
-                min(w, self.crop_size[1]) + 1)
-            return crop_h, crop_w
-        elif self.crop_type == 'relative':
-            crop_h, crop_w = self.crop_size
-            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
-        elif self.crop_type == 'relative_range':
-            crop_size = np.asarray(self.crop_size, dtype=np.float32)
-            crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size)
-            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
-
-    def __call__(self, results):
-        """Call function to randomly crop images, bounding boxes, masks,
-        semantic segmentation maps.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Randomly cropped results, 'img_shape' key in result dict is
-                updated according to crop size.
-        """
-        image_size = results['img'].shape[:2]
-        crop_size = self._get_crop_size(image_size)
-        results = self._crop_data(results, crop_size, self.allow_negative_crop)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(crop_size={self.crop_size}, '
-        repr_str += f'crop_type={self.crop_type}, '
-        repr_str += f'allow_negative_crop={self.allow_negative_crop}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class SegRescale(object):
-    """Rescale semantic segmentation maps.
-
-    Args:
-        scale_factor (float): The scale factor of the final output.
-        backend (str): Image rescale backend, choices are 'cv2' and 'pillow'.
-            These two backends generates slightly different results. Defaults
-            to 'cv2'.
-    """
-
-    def __init__(self, scale_factor=1, backend='cv2'):
-        self.scale_factor = scale_factor
-        self.backend = backend
-
-    def __call__(self, results):
-        """Call function to scale the semantic segmentation map.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with semantic segmentation map scaled.
-        """
-
-        for key in results.get('seg_fields', []):
-            if self.scale_factor != 1:
-                results[key] = mmcv.imrescale(
-                    results[key],
-                    self.scale_factor,
-                    interpolation='nearest',
-                    backend=self.backend)
-        return results
-
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
-
-
-@PIPELINES.register_module()
-class PhotoMetricDistortion(object):
-    """Apply photometric distortion to image sequentially, every transformation
-    is applied with a probability of 0.5. The position of random contrast is in
-    second or second to last.
-
-    1. random brightness
-    2. random contrast (mode 0)
-    3. convert color from BGR to HSV
-    4. random saturation
-    5. random hue
-    6. convert color from HSV to BGR
-    7. random contrast (mode 1)
-    8. randomly swap channels
-
-    Args:
-        brightness_delta (int): delta of brightness.
-        contrast_range (tuple): range of contrast.
-        saturation_range (tuple): range of saturation.
-        hue_delta (int): delta of hue.
-    """
-
-    def __init__(self,
-                 brightness_delta=32,
-                 contrast_range=(0.5, 1.5),
-                 saturation_range=(0.5, 1.5),
-                 hue_delta=18):
-        self.brightness_delta = brightness_delta
-        self.contrast_lower, self.contrast_upper = contrast_range
-        self.saturation_lower, self.saturation_upper = saturation_range
-        self.hue_delta = hue_delta
-
-    def __call__(self, results):
-        """Call function to perform photometric distortion on images.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images distorted.
-        """
-
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        img = results['img']
-        assert img.dtype == np.float32, \
-            'PhotoMetricDistortion needs the input image of dtype np.float32,'\
-            ' please set "to_float32=True" in "LoadImageFromFile" pipeline'
-        # random brightness
-        if random.randint(2):
-            delta = random.uniform(-self.brightness_delta,
-                                   self.brightness_delta)
-            img += delta
-
-        # mode == 0 --> do random contrast first
-        # mode == 1 --> do random contrast last
-        mode = random.randint(2)
-        if mode == 1:
-            if random.randint(2):
-                alpha = random.uniform(self.contrast_lower,
-                                       self.contrast_upper)
-                img *= alpha
-
-        # convert color from BGR to HSV
-        img = mmcv.bgr2hsv(img)
-
-        # random saturation
-        if random.randint(2):
-            img[..., 1] *= random.uniform(self.saturation_lower,
-                                          self.saturation_upper)
-
-        # random hue
-        if random.randint(2):
-            img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
-            img[..., 0][img[..., 0] > 360] -= 360
-            img[..., 0][img[..., 0] < 0] += 360
-
-        # convert color from HSV to BGR
-        img = mmcv.hsv2bgr(img)
-
-        # random contrast
-        if mode == 0:
-            if random.randint(2):
-                alpha = random.uniform(self.contrast_lower,
-                                       self.contrast_upper)
-                img *= alpha
-
-        # randomly swap channels
-        if random.randint(2):
-            img = img[..., random.permutation(3)]
-
-        results['img'] = img
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(\nbrightness_delta={self.brightness_delta},\n'
-        repr_str += 'contrast_range='
-        repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n'
-        repr_str += 'saturation_range='
-        repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n'
-        repr_str += f'hue_delta={self.hue_delta})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Expand(object):
-    """Random expand the image & bboxes.
-
-    Randomly place the original image on a canvas of 'ratio' x original image
-    size filled with mean values. The ratio is in the range of ratio_range.
-
-    Args:
-        mean (tuple): mean value of dataset.
-        to_rgb (bool): if need to convert the order of mean to align with RGB.
-        ratio_range (tuple): range of expand ratio.
-        prob (float): probability of applying this transformation
-    """
-
-    def __init__(self,
-                 mean=(0, 0, 0),
-                 to_rgb=True,
-                 ratio_range=(1, 4),
-                 seg_ignore_label=None,
-                 prob=0.5):
-        self.to_rgb = to_rgb
-        self.ratio_range = ratio_range
-        if to_rgb:
-            self.mean = mean[::-1]
-        else:
-            self.mean = mean
-        self.min_ratio, self.max_ratio = ratio_range
-        self.seg_ignore_label = seg_ignore_label
-        self.prob = prob
-
-    def __call__(self, results):
-        """Call function to expand images, bounding boxes.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images, bounding boxes expanded
-        """
-
-        if random.uniform(0, 1) > self.prob:
-            return results
-
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        img = results['img']
-
-        h, w, c = img.shape
-        ratio = random.uniform(self.min_ratio, self.max_ratio)
-        # speedup expand when meets large image
-        if np.all(self.mean == self.mean[0]):
-            expand_img = np.empty((int(h * ratio), int(w * ratio), c),
-                                  img.dtype)
-            expand_img.fill(self.mean[0])
-        else:
-            expand_img = np.full((int(h * ratio), int(w * ratio), c),
-                                 self.mean,
-                                 dtype=img.dtype)
-        left = int(random.uniform(0, w * ratio - w))
-        top = int(random.uniform(0, h * ratio - h))
-        expand_img[top:top + h, left:left + w] = img
-
-        results['img'] = expand_img
-        # expand bboxes
-        for key in results.get('bbox_fields', []):
-            results[key] = results[key] + np.tile(
-                (left, top), 2).astype(results[key].dtype)
-
-        # expand masks
-        for key in results.get('mask_fields', []):
-            results[key] = results[key].expand(
-                int(h * ratio), int(w * ratio), top, left)
-
-        # expand segs
-        for key in results.get('seg_fields', []):
-            gt_seg = results[key]
-            expand_gt_seg = np.full((int(h * ratio), int(w * ratio)),
-                                    self.seg_ignore_label,
-                                    dtype=gt_seg.dtype)
-            expand_gt_seg[top:top + h, left:left + w] = gt_seg
-            results[key] = expand_gt_seg
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(mean={self.mean}, to_rgb={self.to_rgb}, '
-        repr_str += f'ratio_range={self.ratio_range}, '
-        repr_str += f'seg_ignore_label={self.seg_ignore_label})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class MinIoURandomCrop(object):
-    """Random crop the image & bboxes, the cropped patches have minimum IoU
-    requirement with original image & bboxes, the IoU threshold is randomly
-    selected from min_ious.
-
-    Args:
-        min_ious (tuple): minimum IoU threshold for all intersections with
-        bounding boxes
-        min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w,
-        where a >= min_crop_size).
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-
-    Note:
-        The keys for bboxes, labels and masks should be paired. That is, \
-        `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and \
-        `gt_bboxes_ignore` to `gt_labels_ignore` and `gt_masks_ignore`.
-    """
-
-    def __init__(self,
-                 min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
-                 min_crop_size=0.3,
-                 bbox_clip_border=True):
-        # 1: return ori img
-        self.min_ious = min_ious
-        self.sample_mode = (1, *min_ious, 0)
-        self.min_crop_size = min_crop_size
-        self.bbox_clip_border = bbox_clip_border
-        self.bbox2label = {
-            'gt_bboxes': 'gt_labels',
-            'gt_bboxes_ignore': 'gt_labels_ignore'
-        }
-        self.bbox2mask = {
-            'gt_bboxes': 'gt_masks',
-            'gt_bboxes_ignore': 'gt_masks_ignore'
-        }
-
-    def __call__(self, results):
-        """Call function to crop images and bounding boxes with minimum IoU
-        constraint.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images and bounding boxes cropped, \
-                'img_shape' key is updated.
-        """
-
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        img = results['img']
-        assert 'bbox_fields' in results
-        boxes = [results[key] for key in results['bbox_fields']]
-        boxes = np.concatenate(boxes, 0)
-        h, w, c = img.shape
-        while True:
-            mode = random.choice(self.sample_mode)
-            self.mode = mode
-            if mode == 1:
-                return results
-
-            min_iou = mode
-            for i in range(50):
-                new_w = random.uniform(self.min_crop_size * w, w)
-                new_h = random.uniform(self.min_crop_size * h, h)
-
-                # h / w in [0.5, 2]
-                if new_h / new_w < 0.5 or new_h / new_w > 2:
-                    continue
-
-                left = random.uniform(w - new_w)
-                top = random.uniform(h - new_h)
-
-                patch = np.array(
-                    (int(left), int(top), int(left + new_w), int(top + new_h)))
-                # Line or point crop is not allowed
-                if patch[2] == patch[0] or patch[3] == patch[1]:
-                    continue
-                overlaps = bbox_overlaps(
-                    patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
-                if len(overlaps) > 0 and overlaps.min() < min_iou:
-                    continue
-
-                # center of boxes should inside the crop img
-                # only adjust boxes and instance masks when the gt is not empty
-                if len(overlaps) > 0:
-                    # adjust boxes
-                    def is_center_of_bboxes_in_patch(boxes, patch):
-                        center = (boxes[:, :2] + boxes[:, 2:]) / 2
-                        mask = ((center[:, 0] > patch[0]) *
-                                (center[:, 1] > patch[1]) *
-                                (center[:, 0] < patch[2]) *
-                                (center[:, 1] < patch[3]))
-                        return mask
-
-                    mask = is_center_of_bboxes_in_patch(boxes, patch)
-                    if not mask.any():
-                        continue
-                    for key in results.get('bbox_fields', []):
-                        boxes = results[key].copy()
-                        mask = is_center_of_bboxes_in_patch(boxes, patch)
-                        boxes = boxes[mask]
-                        if self.bbox_clip_border:
-                            boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
-                            boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
-                        boxes -= np.tile(patch[:2], 2)
-
-                        results[key] = boxes
-                        # labels
-                        label_key = self.bbox2label.get(key)
-                        if label_key in results:
-                            results[label_key] = results[label_key][mask]
-
-                        # mask fields
-                        mask_key = self.bbox2mask.get(key)
-                        if mask_key in results:
-                            results[mask_key] = results[mask_key][
-                                mask.nonzero()[0]].crop(patch)
-                # adjust the img no matter whether the gt is empty before crop
-                img = img[patch[1]:patch[3], patch[0]:patch[2]]
-                results['img'] = img
-                results['img_shape'] = img.shape
-
-                # seg fields
-                for key in results.get('seg_fields', []):
-                    results[key] = results[key][patch[1]:patch[3],
-                                                patch[0]:patch[2]]
-                return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(min_ious={self.min_ious}, '
-        repr_str += f'min_crop_size={self.min_crop_size}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Corrupt(object):
-    """Corruption augmentation.
-
-    Corruption transforms implemented based on
-    `imagecorruptions <https://github.com/bethgelab/imagecorruptions>`_.
-
-    Args:
-        corruption (str): Corruption name.
-        severity (int, optional): The severity of corruption. Default: 1.
-    """
-
-    def __init__(self, corruption, severity=1):
-        self.corruption = corruption
-        self.severity = severity
-
-    def __call__(self, results):
-        """Call function to corrupt image.
-
-        Args:
-            results (dict): Result dict from loading pipeline.
-
-        Returns:
-            dict: Result dict with images corrupted.
-        """
-
-        if corrupt is None:
-            raise RuntimeError('imagecorruptions is not installed')
-        if 'img_fields' in results:
-            assert results['img_fields'] == ['img'], \
-                'Only single img_fields is allowed'
-        results['img'] = corrupt(
-            results['img'].astype(np.uint8),
-            corruption_name=self.corruption,
-            severity=self.severity)
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(corruption={self.corruption}, '
-        repr_str += f'severity={self.severity})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class Albu(object):
-    """Albumentation augmentation.
-
-    Adds custom transformations from Albumentations library.
-    Please, visit `https://albumentations.readthedocs.io`
-    to get more information.
-
-    An example of ``transforms`` is as followed:
-
-    .. code-block::
-
-        [
-            dict(
-                type='ShiftScaleRotate',
-                shift_limit=0.0625,
-                scale_limit=0.0,
-                rotate_limit=0,
-                interpolation=1,
-                p=0.5),
-            dict(
-                type='RandomBrightnessContrast',
-                brightness_limit=[0.1, 0.3],
-                contrast_limit=[0.1, 0.3],
-                p=0.2),
-            dict(type='ChannelShuffle', p=0.1),
-            dict(
-                type='OneOf',
-                transforms=[
-                    dict(type='Blur', blur_limit=3, p=1.0),
-                    dict(type='MedianBlur', blur_limit=3, p=1.0)
-                ],
-                p=0.1),
-        ]
-
-    Args:
-        transforms (list[dict]): A list of albu transformations
-        bbox_params (dict): Bbox_params for albumentation `Compose`
-        keymap (dict): Contains {'input key':'albumentation-style key'}
-        skip_img_without_anno (bool): Whether to skip the image if no ann left
-            after aug
-    """
-
-    def __init__(self,
-                 transforms,
-                 bbox_params=None,
-                 keymap=None,
-                 update_pad_shape=False,
-                 skip_img_without_anno=False):
-        if Compose is None:
-            raise RuntimeError('albumentations is not installed')
-
-        # Args will be modified later, copying it will be safer
-        transforms = copy.deepcopy(transforms)
-        if bbox_params is not None:
-            bbox_params = copy.deepcopy(bbox_params)
-        if keymap is not None:
-            keymap = copy.deepcopy(keymap)
-        self.transforms = transforms
-        self.filter_lost_elements = False
-        self.update_pad_shape = update_pad_shape
-        self.skip_img_without_anno = skip_img_without_anno
-
-        # A simple workaround to remove masks without boxes
-        if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
-                and 'filter_lost_elements' in bbox_params):
-            self.filter_lost_elements = True
-            self.origin_label_fields = bbox_params['label_fields']
-            bbox_params['label_fields'] = ['idx_mapper']
-            del bbox_params['filter_lost_elements']
-
-        self.bbox_params = (
-            self.albu_builder(bbox_params) if bbox_params else None)
-        self.aug = Compose([self.albu_builder(t) for t in self.transforms],
-                           bbox_params=self.bbox_params)
-
-        if not keymap:
-            self.keymap_to_albu = {
-                'img': 'image',
-                'gt_masks': 'masks',
-                'gt_bboxes': 'bboxes'
-            }
-        else:
-            self.keymap_to_albu = keymap
-        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
-
-    def albu_builder(self, cfg):
-        """Import a module from albumentations.
-
-        It inherits some of :func:`build_from_cfg` logic.
-
-        Args:
-            cfg (dict): Config dict. It should at least contain the key "type".
-
-        Returns:
-            obj: The constructed object.
-        """
-
-        assert isinstance(cfg, dict) and 'type' in cfg
-        args = cfg.copy()
-
-        obj_type = args.pop('type')
-        if mmcv.is_str(obj_type):
-            if albumentations is None:
-                raise RuntimeError('albumentations is not installed')
-            obj_cls = getattr(albumentations, obj_type)
-        elif inspect.isclass(obj_type):
-            obj_cls = obj_type
-        else:
-            raise TypeError(
-                f'type must be a str or valid type, but got {type(obj_type)}')
-
-        if 'transforms' in args:
-            args['transforms'] = [
-                self.albu_builder(transform)
-                for transform in args['transforms']
-            ]
-
-        return obj_cls(**args)
-
-    @staticmethod
-    def mapper(d, keymap):
-        """Dictionary mapper. Renames keys according to keymap provided.
-
-        Args:
-            d (dict): old dict
-            keymap (dict): {'old_key':'new_key'}
-        Returns:
-            dict: new dict.
-        """
-
-        updated_dict = {}
-        for k, v in zip(d.keys(), d.values()):
-            new_k = keymap.get(k, k)
-            updated_dict[new_k] = d[k]
-        return updated_dict
-
-    def __call__(self, results):
-        # dict to albumentations format
-        results = self.mapper(results, self.keymap_to_albu)
-        # TODO: add bbox_fields
-        if 'bboxes' in results:
-            # to list of boxes
-            if isinstance(results['bboxes'], np.ndarray):
-                results['bboxes'] = [x for x in results['bboxes']]
-            # add pseudo-field for filtration
-            if self.filter_lost_elements:
-                results['idx_mapper'] = np.arange(len(results['bboxes']))
-
-        # TODO: Support mask structure in albu
-        if 'masks' in results:
-            if isinstance(results['masks'], PolygonMasks):
-                raise NotImplementedError(
-                    'Albu only supports BitMap masks now')
-            ori_masks = results['masks']
-            if albumentations.__version__ < '0.5':
-                results['masks'] = results['masks'].masks
-            else:
-                results['masks'] = [mask for mask in results['masks'].masks]
-
-        results = self.aug(**results)
-
-        if 'bboxes' in results:
-            if isinstance(results['bboxes'], list):
-                results['bboxes'] = np.array(
-                    results['bboxes'], dtype=np.float32)
-            results['bboxes'] = results['bboxes'].reshape(-1, 4)
-
-            # filter label_fields
-            if self.filter_lost_elements:
-
-                for label in self.origin_label_fields:
-                    results[label] = np.array(
-                        [results[label][i] for i in results['idx_mapper']])
-                if 'masks' in results:
-                    results['masks'] = np.array(
-                        [results['masks'][i] for i in results['idx_mapper']])
-                    results['masks'] = ori_masks.__class__(
-                        results['masks'], results['image'].shape[0],
-                        results['image'].shape[1])
-
-                if (not len(results['idx_mapper'])
-                        and self.skip_img_without_anno):
-                    return None
-
-        if 'gt_labels' in results:
-            if isinstance(results['gt_labels'], list):
-                results['gt_labels'] = np.array(results['gt_labels'])
-            results['gt_labels'] = results['gt_labels'].astype(np.int64)
-
-        # back to the original format
-        results = self.mapper(results, self.keymap_back)
-
-        # update final shape
-        if self.update_pad_shape:
-            results['pad_shape'] = results['img'].shape
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__ + f'(transforms={self.transforms})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class RandomCenterCropPad(object):
-    """Random center crop and random around padding for CornerNet.
-
-    This operation generates randomly cropped image from the original image and
-    pads it simultaneously. Different from :class:`RandomCrop`, the output
-    shape may not equal to ``crop_size`` strictly. We choose a random value
-    from ``ratios`` and the output shape could be larger or smaller than
-    ``crop_size``. The padding operation is also different from :class:`Pad`,
-    here we use around padding instead of right-bottom padding.
-
-    The relation between output image (padding image) and original image:
-
-    .. code:: text
-
-                        output image
-
-               +----------------------------+
-               |          padded area       |
-        +------|----------------------------|----------+
-        |      |         cropped area       |          |
-        |      |         +---------------+  |          |
-        |      |         |    .   center |  |          | original image
-        |      |         |        range  |  |          |
-        |      |         +---------------+  |          |
-        +------|----------------------------|----------+
-               |          padded area       |
-               +----------------------------+
-
-    There are 5 main areas in the figure:
-
-    - output image: output image of this operation, also called padding
-      image in following instruction.
-    - original image: input image of this operation.
-    - padded area: non-intersect area of output image and original image.
-    - cropped area: the overlap of output image and original image.
-    - center range: a smaller area where random center chosen from.
-      center range is computed by ``border`` and original image's shape
-      to avoid our random center is too close to original image's border.
-
-    Also this operation act differently in train and test mode, the summary
-    pipeline is listed below.
-
-    Train pipeline:
-
-    1. Choose a ``random_ratio`` from ``ratios``, the shape of padding image
-       will be ``random_ratio * crop_size``.
-    2. Choose a ``random_center`` in center range.
-    3. Generate padding image with center matches the ``random_center``.
-    4. Initialize the padding image with pixel value equals to ``mean``.
-    5. Copy the cropped area to padding image.
-    6. Refine annotations.
-
-    Test pipeline:
-
-    1. Compute output shape according to ``test_pad_mode``.
-    2. Generate padding image with center matches the original image
-       center.
-    3. Initialize the padding image with pixel value equals to ``mean``.
-    4. Copy the ``cropped area`` to padding image.
-
-    Args:
-        crop_size (tuple | None): expected size after crop, final size will
-            computed according to ratio. Requires (h, w) in train mode, and
-            None in test mode.
-        ratios (tuple): random select a ratio from tuple and crop image to
-            (crop_size[0] * ratio) * (crop_size[1] * ratio).
-            Only available in train mode.
-        border (int): max distance from center select area to image border.
-            Only available in train mode.
-        mean (sequence): Mean values of 3 channels.
-        std (sequence): Std values of 3 channels.
-        to_rgb (bool): Whether to convert the image from BGR to RGB.
-        test_mode (bool): whether involve random variables in transform.
-            In train mode, crop_size is fixed, center coords and ratio is
-            random selected from predefined lists. In test mode, crop_size
-            is image's original shape, center coords and ratio is fixed.
-        test_pad_mode (tuple): padding method and padding shape value, only
-            available in test mode. Default is using 'logical_or' with
-            127 as padding shape value.
-
-            - 'logical_or': final_shape = input_shape | padding_shape_value
-            - 'size_divisor': final_shape = int(
-              ceil(input_shape / padding_shape_value) * padding_shape_value)
-        bbox_clip_border (bool, optional): Whether clip the objects outside
-            the border of the image. Defaults to True.
-    """
-
-    def __init__(self,
-                 crop_size=None,
-                 ratios=(0.9, 1.0, 1.1),
-                 border=128,
-                 mean=None,
-                 std=None,
-                 to_rgb=None,
-                 test_mode=False,
-                 test_pad_mode=('logical_or', 127),
-                 bbox_clip_border=True):
-        if test_mode:
-            assert crop_size is None, 'crop_size must be None in test mode'
-            assert ratios is None, 'ratios must be None in test mode'
-            assert border is None, 'border must be None in test mode'
-            assert isinstance(test_pad_mode, (list, tuple))
-            assert test_pad_mode[0] in ['logical_or', 'size_divisor']
-        else:
-            assert isinstance(crop_size, (list, tuple))
-            assert crop_size[0] > 0 and crop_size[1] > 0, (
-                'crop_size must > 0 in train mode')
-            assert isinstance(ratios, (list, tuple))
-            assert test_pad_mode is None, (
-                'test_pad_mode must be None in train mode')
-
-        self.crop_size = crop_size
-        self.ratios = ratios
-        self.border = border
-        # We do not set default value to mean, std and to_rgb because these
-        # hyper-parameters are easy to forget but could affect the performance.
-        # Please use the same setting as Normalize for performance assurance.
-        assert mean is not None and std is not None and to_rgb is not None
-        self.to_rgb = to_rgb
-        self.input_mean = mean
-        self.input_std = std
-        if to_rgb:
-            self.mean = mean[::-1]
-            self.std = std[::-1]
-        else:
-            self.mean = mean
-            self.std = std
-        self.test_mode = test_mode
-        self.test_pad_mode = test_pad_mode
-        self.bbox_clip_border = bbox_clip_border
-
-    def _get_border(self, border, size):
-        """Get final border for the target size.
-
-        This function generates a ``final_border`` according to image's shape.
-        The area between ``final_border`` and ``size - final_border`` is the
-        ``center range``. We randomly choose center from the ``center range``
-        to avoid our random center is too close to original image's border.
-        Also ``center range`` should be larger than 0.
-
-        Args:
-            border (int): The initial border, default is 128.
-            size (int): The width or height of original image.
-        Returns:
-            int: The final border.
-        """
-        k = 2 * border / size
-        i = pow(2, np.ceil(np.log2(np.ceil(k))) + (k == int(k)))
-        return border // i
-
-    def _filter_boxes(self, patch, boxes):
-        """Check whether the center of each box is in the patch.
-
-        Args:
-            patch (list[int]): The cropped area, [left, top, right, bottom].
-            boxes (numpy array, (N x 4)): Ground truth boxes.
-
-        Returns:
-            mask (numpy array, (N,)): Each box is inside or outside the patch.
-        """
-        center = (boxes[:, :2] + boxes[:, 2:]) / 2
-        mask = (center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) * (
-            center[:, 0] < patch[2]) * (
-                center[:, 1] < patch[3])
-        return mask
-
-    def _crop_image_and_paste(self, image, center, size):
-        """Crop image with a given center and size, then paste the cropped
-        image to a blank image with two centers align.
-
-        This function is equivalent to generating a blank image with ``size``
-        as its shape. Then cover it on the original image with two centers (
-        the center of blank image and the random center of original image)
-        aligned. The overlap area is paste from the original image and the
-        outside area is filled with ``mean pixel``.
-
-        Args:
-            image (np array, H x W x C): Original image.
-            center (list[int]): Target crop center coord.
-            size (list[int]): Target crop size. [target_h, target_w]
-
-        Returns:
-            cropped_img (np array, target_h x target_w x C): Cropped image.
-            border (np array, 4): The distance of four border of
-                ``cropped_img`` to the original image area, [top, bottom,
-                left, right]
-            patch (list[int]): The cropped area, [left, top, right, bottom].
-        """
-        center_y, center_x = center
-        target_h, target_w = size
-        img_h, img_w, img_c = image.shape
-
-        x0 = max(0, center_x - target_w // 2)
-        x1 = min(center_x + target_w // 2, img_w)
-        y0 = max(0, center_y - target_h // 2)
-        y1 = min(center_y + target_h // 2, img_h)
-        patch = np.array((int(x0), int(y0), int(x1), int(y1)))
-
-        left, right = center_x - x0, x1 - center_x
-        top, bottom = center_y - y0, y1 - center_y
-
-        cropped_center_y, cropped_center_x = target_h // 2, target_w // 2
-        cropped_img = np.zeros((target_h, target_w, img_c), dtype=image.dtype)
-        for i in range(img_c):
-            cropped_img[:, :, i] += self.mean[i]
-        y_slice = slice(cropped_center_y - top, cropped_center_y + bottom)
-        x_slice = slice(cropped_center_x - left, cropped_center_x + right)
-        cropped_img[y_slice, x_slice, :] = image[y0:y1, x0:x1, :]
-
-        border = np.array([
-            cropped_center_y - top, cropped_center_y + bottom,
-            cropped_center_x - left, cropped_center_x + right
-        ],
-                          dtype=np.float32)
-
-        return cropped_img, border, patch
-
-    def _train_aug(self, results):
-        """Random crop and around padding the original image.
-
-        Args:
-            results (dict): Image infomations in the augment pipeline.
-
-        Returns:
-            results (dict): The updated dict.
-        """
-        img = results['img']
-        h, w, c = img.shape
-        boxes = results['gt_bboxes']
-        while True:
-            scale = random.choice(self.ratios)
-            new_h = int(self.crop_size[0] * scale)
-            new_w = int(self.crop_size[1] * scale)
-            h_border = self._get_border(self.border, h)
-            w_border = self._get_border(self.border, w)
-
-            for i in range(50):
-                center_x = random.randint(low=w_border, high=w - w_border)
-                center_y = random.randint(low=h_border, high=h - h_border)
-
-                cropped_img, border, patch = self._crop_image_and_paste(
-                    img, [center_y, center_x], [new_h, new_w])
-
-                mask = self._filter_boxes(patch, boxes)
-                # if image do not have valid bbox, any crop patch is valid.
-                if not mask.any() and len(boxes) > 0:
-                    continue
-
-                results['img'] = cropped_img
-                results['img_shape'] = cropped_img.shape
-                results['pad_shape'] = cropped_img.shape
-
-                x0, y0, x1, y1 = patch
-
-                left_w, top_h = center_x - x0, center_y - y0
-                cropped_center_x, cropped_center_y = new_w // 2, new_h // 2
-
-                # crop bboxes accordingly and clip to the image boundary
-                for key in results.get('bbox_fields', []):
-                    mask = self._filter_boxes(patch, results[key])
-                    bboxes = results[key][mask]
-                    bboxes[:, 0:4:2] += cropped_center_x - left_w - x0
-                    bboxes[:, 1:4:2] += cropped_center_y - top_h - y0
-                    if self.bbox_clip_border:
-                        bboxes[:, 0:4:2] = np.clip(bboxes[:, 0:4:2], 0, new_w)
-                        bboxes[:, 1:4:2] = np.clip(bboxes[:, 1:4:2], 0, new_h)
-                    keep = (bboxes[:, 2] > bboxes[:, 0]) & (
-                        bboxes[:, 3] > bboxes[:, 1])
-                    bboxes = bboxes[keep]
-                    results[key] = bboxes
-                    if key in ['gt_bboxes']:
-                        if 'gt_labels' in results:
-                            labels = results['gt_labels'][mask]
-                            labels = labels[keep]
-                            results['gt_labels'] = labels
-                        if 'gt_masks' in results:
-                            raise NotImplementedError(
-                                'RandomCenterCropPad only supports bbox.')
-
-                # crop semantic seg
-                for key in results.get('seg_fields', []):
-                    raise NotImplementedError(
-                        'RandomCenterCropPad only supports bbox.')
-                return results
-
-    def _test_aug(self, results):
-        """Around padding the original image without cropping.
-
-        The padding mode and value are from ``test_pad_mode``.
-
-        Args:
-            results (dict): Image infomations in the augment pipeline.
-
-        Returns:
-            results (dict): The updated dict.
-        """
-        img = results['img']
-        h, w, c = img.shape
-        results['img_shape'] = img.shape
-        if self.test_pad_mode[0] in ['logical_or']:
-            target_h = h | self.test_pad_mode[1]
-            target_w = w | self.test_pad_mode[1]
-        elif self.test_pad_mode[0] in ['size_divisor']:
-            divisor = self.test_pad_mode[1]
-            target_h = int(np.ceil(h / divisor)) * divisor
-            target_w = int(np.ceil(w / divisor)) * divisor
-        else:
-            raise NotImplementedError(
-                'RandomCenterCropPad only support two testing pad mode:'
-                'logical-or and size_divisor.')
-
-        cropped_img, border, _ = self._crop_image_and_paste(
-            img, [h // 2, w // 2], [target_h, target_w])
-        results['img'] = cropped_img
-        results['pad_shape'] = cropped_img.shape
-        results['border'] = border
-        return results
-
-    def __call__(self, results):
-        img = results['img']
-        assert img.dtype == np.float32, (
-            'RandomCenterCropPad needs the input image of dtype np.float32,'
-            ' please set "to_float32=True" in "LoadImageFromFile" pipeline')
-        h, w, c = img.shape
-        assert c == len(self.mean)
-        if self.test_mode:
-            return self._test_aug(results)
-        else:
-            return self._train_aug(results)
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(crop_size={self.crop_size}, '
-        repr_str += f'ratios={self.ratios}, '
-        repr_str += f'border={self.border}, '
-        repr_str += f'mean={self.input_mean}, '
-        repr_str += f'std={self.input_std}, '
-        repr_str += f'to_rgb={self.to_rgb}, '
-        repr_str += f'test_mode={self.test_mode}, '
-        repr_str += f'test_pad_mode={self.test_pad_mode}, '
-        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
-        return repr_str
-
-
-@PIPELINES.register_module()
-class CutOut(object):
-    """CutOut operation.
-
-    Randomly drop some regions of image used in
-    `Cutout <https://arxiv.org/abs/1708.04552>`_.
-
-    Args:
-        n_holes (int | tuple[int, int]): Number of regions to be dropped.
-            If it is given as a list, number of holes will be randomly
-            selected from the closed interval [`n_holes[0]`, `n_holes[1]`].
-        cutout_shape (tuple[int, int] | list[tuple[int, int]]): The candidate
-            shape of dropped regions. It can be `tuple[int, int]` to use a
-            fixed cutout shape, or `list[tuple[int, int]]` to randomly choose
-            shape from the list.
-        cutout_ratio (tuple[float, float] | list[tuple[float, float]]): The
-            candidate ratio of dropped regions. It can be `tuple[float, float]`
-            to use a fixed ratio or `list[tuple[float, float]]` to randomly
-            choose ratio from the list. Please note that `cutout_shape`
-            and `cutout_ratio` cannot be both given at the same time.
-        fill_in (tuple[float, float, float] | tuple[int, int, int]): The value
-            of pixel to fill in the dropped regions. Default: (0, 0, 0).
-    """
-
-    def __init__(self,
-                 n_holes,
-                 cutout_shape=None,
-                 cutout_ratio=None,
-                 fill_in=(0, 0, 0)):
-
-        assert (cutout_shape is None) ^ (cutout_ratio is None), \
-            'Either cutout_shape or cutout_ratio should be specified.'
-        assert (isinstance(cutout_shape, (list, tuple))
-                or isinstance(cutout_ratio, (list, tuple)))
-        if isinstance(n_holes, tuple):
-            assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
-        else:
-            n_holes = (n_holes, n_holes)
-        self.n_holes = n_holes
-        self.fill_in = fill_in
-        self.with_ratio = cutout_ratio is not None
-        self.candidates = cutout_ratio if self.with_ratio else cutout_shape
-        if not isinstance(self.candidates, list):
-            self.candidates = [self.candidates]
-
-    def __call__(self, results):
-        """Call function to drop some regions of image."""
-        h, w, c = results['img'].shape
-        n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
-        for _ in range(n_holes):
-            x1 = np.random.randint(0, w)
-            y1 = np.random.randint(0, h)
-            index = np.random.randint(0, len(self.candidates))
-            if not self.with_ratio:
-                cutout_w, cutout_h = self.candidates[index]
-            else:
-                cutout_w = int(self.candidates[index][0] * w)
-                cutout_h = int(self.candidates[index][1] * h)
-
-            x2 = np.clip(x1 + cutout_w, 0, w)
-            y2 = np.clip(y1 + cutout_h, 0, h)
-            results['img'][y1:y2, x1:x2, :] = self.fill_in
-
-        return results
-
-    def __repr__(self):
-        repr_str = self.__class__.__name__
-        repr_str += f'(n_holes={self.n_holes}, '
-        repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
-                     else f'cutout_shape={self.candidates}, ')
-        repr_str += f'fill_in={self.fill_in})'
-        return repr_str

spaces/CofAI/chat.b4/g4f/Provider/Providers/Theb.py

@@ -1,28 +0,0 @@
-import os
-import json
-import time
-import subprocess
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://theb.ai'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-
-    path = os.path.dirname(os.path.realpath(__file__))
-    config = json.dumps({
-        'messages': messages,
-        'model': model}, separators=(',', ':'))
-    
-    cmd = ['python3', f'{path}/helpers/theb.py', config]
-
-    p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
-
-    for line in iter(p.stdout.readline, b''):
-        yield line.decode('utf-8')
-        
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/_config.py

@@ -1,31 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-
-__all__ = ["set_run_validators", "get_run_validators"]
-
-_run_validators = True
-
-
-def set_run_validators(run):
-    """
-    Set whether or not validators are run.  By default, they are run.
-
-    .. deprecated:: 21.3.0 It will not be removed, but it also will not be
-        moved to new ``attrs`` namespace. Use `attrs.validators.set_disabled()`
-        instead.
-    """
-    if not isinstance(run, bool):
-        raise TypeError("'run' must be bool.")
-    global _run_validators
-    _run_validators = run
-
-
-def get_run_validators():
-    """
-    Return whether or not validators are run.
-
-    .. deprecated:: 21.3.0 It will not be removed, but it also will not be
-        moved to new ``attrs`` namespace. Use `attrs.validators.get_disabled()`
-        instead.
-    """
-    return _run_validators

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/dateutil/parser/_parser.py

@@ -1,1613 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic date/time string parser which is able to parse
-most known formats to represent a date and/or time.
-
-This module attempts to be forgiving with regards to unlikely input formats,
-returning a datetime object even for dates which are ambiguous. If an element
-of a date/time stamp is omitted, the following rules are applied:
-
-- If AM or PM is left unspecified, a 24-hour clock is assumed, however, an hour
-  on a 12-hour clock (``0 <= hour <= 12``) *must* be specified if AM or PM is
-  specified.
-- If a time zone is omitted, a timezone-naive datetime is returned.
-
-If any other elements are missing, they are taken from the
-:class:`datetime.datetime` object passed to the parameter ``default``. If this
-results in a day number exceeding the valid number of days per month, the
-value falls back to the end of the month.
-
-Additional resources about date/time string formats can be found below:
-
-- `A summary of the international standard date and time notation
-  <https://www.cl.cam.ac.uk/~mgk25/iso-time.html>`_
-- `W3C Date and Time Formats <https://www.w3.org/TR/NOTE-datetime>`_
-- `Time Formats (Planetary Rings Node) <https://pds-rings.seti.org:443/tools/time_formats.html>`_
-- `CPAN ParseDate module
-  <https://metacpan.org/pod/release/MUIR/Time-modules-2013.0912/lib/Time/ParseDate.pm>`_
-- `Java SimpleDateFormat Class
-  <https://docs.oracle.com/javase/6/docs/api/java/text/SimpleDateFormat.html>`_
-"""
-from __future__ import unicode_literals
-
-import datetime
-import re
-import string
-import time
-import warnings
-
-from calendar import monthrange
-from io import StringIO
-
-import six
-from six import integer_types, text_type
-
-from decimal import Decimal
-
-from warnings import warn
-
-from .. import relativedelta
-from .. import tz
-
-__all__ = ["parse", "parserinfo", "ParserError"]
-
-
-# TODO: pandas.core.tools.datetimes imports this explicitly.  Might be worth
-# making public and/or figuring out if there is something we can
-# take off their plate.
-class _timelex(object):
-    # Fractional seconds are sometimes split by a comma
-    _split_decimal = re.compile("([.,])")
-
-    def __init__(self, instream):
-        if isinstance(instream, (bytes, bytearray)):
-            instream = instream.decode()
-
-        if isinstance(instream, text_type):
-            instream = StringIO(instream)
-        elif getattr(instream, 'read', None) is None:
-            raise TypeError('Parser must be a string or character stream, not '
-                            '{itype}'.format(itype=instream.__class__.__name__))
-
-        self.instream = instream
-        self.charstack = []
-        self.tokenstack = []
-        self.eof = False
-
-    def get_token(self):
-        """
-        This function breaks the time string into lexical units (tokens), which
-        can be parsed by the parser. Lexical units are demarcated by changes in
-        the character set, so any continuous string of letters is considered
-        one unit, any continuous string of numbers is considered one unit.
-
-        The main complication arises from the fact that dots ('.') can be used
-        both as separators (e.g. "Sep.20.2009") or decimal points (e.g.
-        "4:30:21.447"). As such, it is necessary to read the full context of
-        any dot-separated strings before breaking it into tokens; as such, this
-        function maintains a "token stack", for when the ambiguous context
-        demands that multiple tokens be parsed at once.
-        """
-        if self.tokenstack:
-            return self.tokenstack.pop(0)
-
-        seenletters = False
-        token = None
-        state = None
-
-        while not self.eof:
-            # We only realize that we've reached the end of a token when we
-            # find a character that's not part of the current token - since
-            # that character may be part of the next token, it's stored in the
-            # charstack.
-            if self.charstack:
-                nextchar = self.charstack.pop(0)
-            else:
-                nextchar = self.instream.read(1)
-                while nextchar == '\x00':
-                    nextchar = self.instream.read(1)
-
-            if not nextchar:
-                self.eof = True
-                break
-            elif not state:
-                # First character of the token - determines if we're starting
-                # to parse a word, a number or something else.
-                token = nextchar
-                if self.isword(nextchar):
-                    state = 'a'
-                elif self.isnum(nextchar):
-                    state = '0'
-                elif self.isspace(nextchar):
-                    token = ' '
-                    break  # emit token
-                else:
-                    break  # emit token
-            elif state == 'a':
-                # If we've already started reading a word, we keep reading
-                # letters until we find something that's not part of a word.
-                seenletters = True
-                if self.isword(nextchar):
-                    token += nextchar
-                elif nextchar == '.':
-                    token += nextchar
-                    state = 'a.'
-                else:
-                    self.charstack.append(nextchar)
-                    break  # emit token
-            elif state == '0':
-                # If we've already started reading a number, we keep reading
-                # numbers until we find something that doesn't fit.
-                if self.isnum(nextchar):
-                    token += nextchar
-                elif nextchar == '.' or (nextchar == ',' and len(token) >= 2):
-                    token += nextchar
-                    state = '0.'
-                else:
-                    self.charstack.append(nextchar)
-                    break  # emit token
-            elif state == 'a.':
-                # If we've seen some letters and a dot separator, continue
-                # parsing, and the tokens will be broken up later.
-                seenletters = True
-                if nextchar == '.' or self.isword(nextchar):
-                    token += nextchar
-                elif self.isnum(nextchar) and token[-1] == '.':
-                    token += nextchar
-                    state = '0.'
-                else:
-                    self.charstack.append(nextchar)
-                    break  # emit token
-            elif state == '0.':
-                # If we've seen at least one dot separator, keep going, we'll
-                # break up the tokens later.
-                if nextchar == '.' or self.isnum(nextchar):
-                    token += nextchar
-                elif self.isword(nextchar) and token[-1] == '.':
-                    token += nextchar
-                    state = 'a.'
-                else:
-                    self.charstack.append(nextchar)
-                    break  # emit token
-
-        if (state in ('a.', '0.') and (seenletters or token.count('.') > 1 or
-                                       token[-1] in '.,')):
-            l = self._split_decimal.split(token)
-            token = l[0]
-            for tok in l[1:]:
-                if tok:
-                    self.tokenstack.append(tok)
-
-        if state == '0.' and token.count('.') == 0:
-            token = token.replace(',', '.')
-
-        return token
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        token = self.get_token()
-        if token is None:
-            raise StopIteration
-
-        return token
-
-    def next(self):
-        return self.__next__()  # Python 2.x support
-
-    @classmethod
-    def split(cls, s):
-        return list(cls(s))
-
-    @classmethod
-    def isword(cls, nextchar):
-        """ Whether or not the next character is part of a word """
-        return nextchar.isalpha()
-
-    @classmethod
-    def isnum(cls, nextchar):
-        """ Whether the next character is part of a number """
-        return nextchar.isdigit()
-
-    @classmethod
-    def isspace(cls, nextchar):
-        """ Whether the next character is whitespace """
-        return nextchar.isspace()
-
-
-class _resultbase(object):
-
-    def __init__(self):
-        for attr in self.__slots__:
-            setattr(self, attr, None)
-
-    def _repr(self, classname):
-        l = []
-        for attr in self.__slots__:
-            value = getattr(self, attr)
-            if value is not None:
-                l.append("%s=%s" % (attr, repr(value)))
-        return "%s(%s)" % (classname, ", ".join(l))
-
-    def __len__(self):
-        return (sum(getattr(self, attr) is not None
-                    for attr in self.__slots__))
-
-    def __repr__(self):
-        return self._repr(self.__class__.__name__)
-
-
-class parserinfo(object):
-    """
-    Class which handles what inputs are accepted. Subclass this to customize
-    the language and acceptable values for each parameter.
-
-    :param dayfirst:
-        Whether to interpret the first value in an ambiguous 3-integer date
-        (e.g. 01/05/09) as the day (``True``) or month (``False``). If
-        ``yearfirst`` is set to ``True``, this distinguishes between YDM
-        and YMD. Default is ``False``.
-
-    :param yearfirst:
-        Whether to interpret the first value in an ambiguous 3-integer date
-        (e.g. 01/05/09) as the year. If ``True``, the first number is taken
-        to be the year, otherwise the last number is taken to be the year.
-        Default is ``False``.
-    """
-
-    # m from a.m/p.m, t from ISO T separator
-    JUMP = [" ", ".", ",", ";", "-", "/", "'",
-            "at", "on", "and", "ad", "m", "t", "of",
-            "st", "nd", "rd", "th"]
-
-    WEEKDAYS = [("Mon", "Monday"),
-                ("Tue", "Tuesday"),     # TODO: "Tues"
-                ("Wed", "Wednesday"),
-                ("Thu", "Thursday"),    # TODO: "Thurs"
-                ("Fri", "Friday"),
-                ("Sat", "Saturday"),
-                ("Sun", "Sunday")]
-    MONTHS = [("Jan", "January"),
-              ("Feb", "February"),      # TODO: "Febr"
-              ("Mar", "March"),
-              ("Apr", "April"),
-              ("May", "May"),
-              ("Jun", "June"),
-              ("Jul", "July"),
-              ("Aug", "August"),
-              ("Sep", "Sept", "September"),
-              ("Oct", "October"),
-              ("Nov", "November"),
-              ("Dec", "December")]
-    HMS = [("h", "hour", "hours"),
-           ("m", "minute", "minutes"),
-           ("s", "second", "seconds")]
-    AMPM = [("am", "a"),
-            ("pm", "p")]
-    UTCZONE = ["UTC", "GMT", "Z", "z"]
-    PERTAIN = ["of"]
-    TZOFFSET = {}
-    # TODO: ERA = ["AD", "BC", "CE", "BCE", "Stardate",
-    #              "Anno Domini", "Year of Our Lord"]
-
-    def __init__(self, dayfirst=False, yearfirst=False):
-        self._jump = self._convert(self.JUMP)
-        self._weekdays = self._convert(self.WEEKDAYS)
-        self._months = self._convert(self.MONTHS)
-        self._hms = self._convert(self.HMS)
-        self._ampm = self._convert(self.AMPM)
-        self._utczone = self._convert(self.UTCZONE)
-        self._pertain = self._convert(self.PERTAIN)
-
-        self.dayfirst = dayfirst
-        self.yearfirst = yearfirst
-
-        self._year = time.localtime().tm_year
-        self._century = self._year // 100 * 100
-
-    def _convert(self, lst):
-        dct = {}
-        for i, v in enumerate(lst):
-            if isinstance(v, tuple):
-                for v in v:
-                    dct[v.lower()] = i
-            else:
-                dct[v.lower()] = i
-        return dct
-
-    def jump(self, name):
-        return name.lower() in self._jump
-
-    def weekday(self, name):
-        try:
-            return self._weekdays[name.lower()]
-        except KeyError:
-            pass
-        return None
-
-    def month(self, name):
-        try:
-            return self._months[name.lower()] + 1
-        except KeyError:
-            pass
-        return None
-
-    def hms(self, name):
-        try:
-            return self._hms[name.lower()]
-        except KeyError:
-            return None
-
-    def ampm(self, name):
-        try:
-            return self._ampm[name.lower()]
-        except KeyError:
-            return None
-
-    def pertain(self, name):
-        return name.lower() in self._pertain
-
-    def utczone(self, name):
-        return name.lower() in self._utczone
-
-    def tzoffset(self, name):
-        if name in self._utczone:
-            return 0
-
-        return self.TZOFFSET.get(name)
-
-    def convertyear(self, year, century_specified=False):
-        """
-        Converts two-digit years to year within [-50, 49]
-        range of self._year (current local time)
-        """
-
-        # Function contract is that the year is always positive
-        assert year >= 0
-
-        if year < 100 and not century_specified:
-            # assume current century to start
-            year += self._century
-
-            if year >= self._year + 50:  # if too far in future
-                year -= 100
-            elif year < self._year - 50:  # if too far in past
-                year += 100
-
-        return year
-
-    def validate(self, res):
-        # move to info
-        if res.year is not None:
-            res.year = self.convertyear(res.year, res.century_specified)
-
-        if ((res.tzoffset == 0 and not res.tzname) or
-             (res.tzname == 'Z' or res.tzname == 'z')):
-            res.tzname = "UTC"
-            res.tzoffset = 0
-        elif res.tzoffset != 0 and res.tzname and self.utczone(res.tzname):
-            res.tzoffset = 0
-        return True
-
-
-class _ymd(list):
-    def __init__(self, *args, **kwargs):
-        super(self.__class__, self).__init__(*args, **kwargs)
-        self.century_specified = False
-        self.dstridx = None
-        self.mstridx = None
-        self.ystridx = None
-
-    @property
-    def has_year(self):
-        return self.ystridx is not None
-
-    @property
-    def has_month(self):
-        return self.mstridx is not None
-
-    @property
-    def has_day(self):
-        return self.dstridx is not None
-
-    def could_be_day(self, value):
-        if self.has_day:
-            return False
-        elif not self.has_month:
-            return 1 <= value <= 31
-        elif not self.has_year:
-            # Be permissive, assume leap year
-            month = self[self.mstridx]
-            return 1 <= value <= monthrange(2000, month)[1]
-        else:
-            month = self[self.mstridx]
-            year = self[self.ystridx]
-            return 1 <= value <= monthrange(year, month)[1]
-
-    def append(self, val, label=None):
-        if hasattr(val, '__len__'):
-            if val.isdigit() and len(val) > 2:
-                self.century_specified = True
-                if label not in [None, 'Y']:  # pragma: no cover
-                    raise ValueError(label)
-                label = 'Y'
-        elif val > 100:
-            self.century_specified = True
-            if label not in [None, 'Y']:  # pragma: no cover
-                raise ValueError(label)
-            label = 'Y'
-
-        super(self.__class__, self).append(int(val))
-
-        if label == 'M':
-            if self.has_month:
-                raise ValueError('Month is already set')
-            self.mstridx = len(self) - 1
-        elif label == 'D':
-            if self.has_day:
-                raise ValueError('Day is already set')
-            self.dstridx = len(self) - 1
-        elif label == 'Y':
-            if self.has_year:
-                raise ValueError('Year is already set')
-            self.ystridx = len(self) - 1
-
-    def _resolve_from_stridxs(self, strids):
-        """
-        Try to resolve the identities of year/month/day elements using
-        ystridx, mstridx, and dstridx, if enough of these are specified.
-        """
-        if len(self) == 3 and len(strids) == 2:
-            # we can back out the remaining stridx value
-            missing = [x for x in range(3) if x not in strids.values()]
-            key = [x for x in ['y', 'm', 'd'] if x not in strids]
-            assert len(missing) == len(key) == 1
-            key = key[0]
-            val = missing[0]
-            strids[key] = val
-
-        assert len(self) == len(strids)  # otherwise this should not be called
-        out = {key: self[strids[key]] for key in strids}
-        return (out.get('y'), out.get('m'), out.get('d'))
-
-    def resolve_ymd(self, yearfirst, dayfirst):
-        len_ymd = len(self)
-        year, month, day = (None, None, None)
-
-        strids = (('y', self.ystridx),
-                  ('m', self.mstridx),
-                  ('d', self.dstridx))
-
-        strids = {key: val for key, val in strids if val is not None}
-        if (len(self) == len(strids) > 0 or
-                (len(self) == 3 and len(strids) == 2)):
-            return self._resolve_from_stridxs(strids)
-
-        mstridx = self.mstridx
-
-        if len_ymd > 3:
-            raise ValueError("More than three YMD values")
-        elif len_ymd == 1 or (mstridx is not None and len_ymd == 2):
-            # One member, or two members with a month string
-            if mstridx is not None:
-                month = self[mstridx]
-                # since mstridx is 0 or 1, self[mstridx-1] always
-                # looks up the other element
-                other = self[mstridx - 1]
-            else:
-                other = self[0]
-
-            if len_ymd > 1 or mstridx is None:
-                if other > 31:
-                    year = other
-                else:
-                    day = other
-
-        elif len_ymd == 2:
-            # Two members with numbers
-            if self[0] > 31:
-                # 99-01
-                year, month = self
-            elif self[1] > 31:
-                # 01-99
-                month, year = self
-            elif dayfirst and self[1] <= 12:
-                # 13-01
-                day, month = self
-            else:
-                # 01-13
-                month, day = self
-
-        elif len_ymd == 3:
-            # Three members
-            if mstridx == 0:
-                if self[1] > 31:
-                    # Apr-2003-25
-                    month, year, day = self
-                else:
-                    month, day, year = self
-            elif mstridx == 1:
-                if self[0] > 31 or (yearfirst and self[2] <= 31):
-                    # 99-Jan-01
-                    year, month, day = self
-                else:
-                    # 01-Jan-01
-                    # Give precedence to day-first, since
-                    # two-digit years is usually hand-written.
-                    day, month, year = self
-
-            elif mstridx == 2:
-                # WTF!?
-                if self[1] > 31:
-                    # 01-99-Jan
-                    day, year, month = self
-                else:
-                    # 99-01-Jan
-                    year, day, month = self
-
-            else:
-                if (self[0] > 31 or
-                    self.ystridx == 0 or
-                        (yearfirst and self[1] <= 12 and self[2] <= 31)):
-                    # 99-01-01
-                    if dayfirst and self[2] <= 12:
-                        year, day, month = self
-                    else:
-                        year, month, day = self
-                elif self[0] > 12 or (dayfirst and self[1] <= 12):
-                    # 13-01-01
-                    day, month, year = self
-                else:
-                    # 01-13-01
-                    month, day, year = self
-
-        return year, month, day
-
-
-class parser(object):
-    def __init__(self, info=None):
-        self.info = info or parserinfo()
-
-    def parse(self, timestr, default=None,
-              ignoretz=False, tzinfos=None, **kwargs):
-        """
-        Parse the date/time string into a :class:`datetime.datetime` object.
-
-        :param timestr:
-            Any date/time string using the supported formats.
-
-        :param default:
-            The default datetime object, if this is a datetime object and not
-            ``None``, elements specified in ``timestr`` replace elements in the
-            default object.
-
-        :param ignoretz:
-            If set ``True``, time zones in parsed strings are ignored and a
-            naive :class:`datetime.datetime` object is returned.
-
-        :param tzinfos:
-            Additional time zone names / aliases which may be present in the
-            string. This argument maps time zone names (and optionally offsets
-            from those time zones) to time zones. This parameter can be a
-            dictionary with timezone aliases mapping time zone names to time
-            zones or a function taking two parameters (``tzname`` and
-            ``tzoffset``) and returning a time zone.
-
-            The timezones to which the names are mapped can be an integer
-            offset from UTC in seconds or a :class:`tzinfo` object.
-
-            .. doctest::
-               :options: +NORMALIZE_WHITESPACE
-
-                >>> from dateutil.parser import parse
-                >>> from dateutil.tz import gettz
-                >>> tzinfos = {"BRST": -7200, "CST": gettz("America/Chicago")}
-                >>> parse("2012-01-19 17:21:00 BRST", tzinfos=tzinfos)
-                datetime.datetime(2012, 1, 19, 17, 21, tzinfo=tzoffset(u'BRST', -7200))
-                >>> parse("2012-01-19 17:21:00 CST", tzinfos=tzinfos)
-                datetime.datetime(2012, 1, 19, 17, 21,
-                                  tzinfo=tzfile('/usr/share/zoneinfo/America/Chicago'))
-
-            This parameter is ignored if ``ignoretz`` is set.
-
-        :param \\*\\*kwargs:
-            Keyword arguments as passed to ``_parse()``.
-
-        :return:
-            Returns a :class:`datetime.datetime` object or, if the
-            ``fuzzy_with_tokens`` option is ``True``, returns a tuple, the
-            first element being a :class:`datetime.datetime` object, the second
-            a tuple containing the fuzzy tokens.
-
-        :raises ParserError:
-            Raised for invalid or unknown string format, if the provided
-            :class:`tzinfo` is not in a valid format, or if an invalid date
-            would be created.
-
-        :raises TypeError:
-            Raised for non-string or character stream input.
-
-        :raises OverflowError:
-            Raised if the parsed date exceeds the largest valid C integer on
-            your system.
-        """
-
-        if default is None:
-            default = datetime.datetime.now().replace(hour=0, minute=0,
-                                                      second=0, microsecond=0)
-
-        res, skipped_tokens = self._parse(timestr, **kwargs)
-
-        if res is None:
-            raise ParserError("Unknown string format: %s", timestr)
-
-        if len(res) == 0:
-            raise ParserError("String does not contain a date: %s", timestr)
-
-        try:
-            ret = self._build_naive(res, default)
-        except ValueError as e:
-            six.raise_from(ParserError(str(e) + ": %s", timestr), e)
-
-        if not ignoretz:
-            ret = self._build_tzaware(ret, res, tzinfos)
-
-        if kwargs.get('fuzzy_with_tokens', False):
-            return ret, skipped_tokens
-        else:
-            return ret
-
-    class _result(_resultbase):
-        __slots__ = ["year", "month", "day", "weekday",
-                     "hour", "minute", "second", "microsecond",
-                     "tzname", "tzoffset", "ampm","any_unused_tokens"]
-
-    def _parse(self, timestr, dayfirst=None, yearfirst=None, fuzzy=False,
-               fuzzy_with_tokens=False):
-        """
-        Private method which performs the heavy lifting of parsing, called from
-        ``parse()``, which passes on its ``kwargs`` to this function.
-
-        :param timestr:
-            The string to parse.
-
-        :param dayfirst:
-            Whether to interpret the first value in an ambiguous 3-integer date
-            (e.g. 01/05/09) as the day (``True``) or month (``False``). If
-            ``yearfirst`` is set to ``True``, this distinguishes between YDM
-            and YMD. If set to ``None``, this value is retrieved from the
-            current :class:`parserinfo` object (which itself defaults to
-            ``False``).
-
-        :param yearfirst:
-            Whether to interpret the first value in an ambiguous 3-integer date
-            (e.g. 01/05/09) as the year. If ``True``, the first number is taken
-            to be the year, otherwise the last number is taken to be the year.
-            If this is set to ``None``, the value is retrieved from the current
-            :class:`parserinfo` object (which itself defaults to ``False``).
-
-        :param fuzzy:
-            Whether to allow fuzzy parsing, allowing for string like "Today is
-            January 1, 2047 at 8:21:00AM".
-
-        :param fuzzy_with_tokens:
-            If ``True``, ``fuzzy`` is automatically set to True, and the parser
-            will return a tuple where the first element is the parsed
-            :class:`datetime.datetime` datetimestamp and the second element is
-            a tuple containing the portions of the string which were ignored:
-
-            .. doctest::
-
-                >>> from dateutil.parser import parse
-                >>> parse("Today is January 1, 2047 at 8:21:00AM", fuzzy_with_tokens=True)
-                (datetime.datetime(2047, 1, 1, 8, 21), (u'Today is ', u' ', u'at '))
-
-        """
-        if fuzzy_with_tokens:
-            fuzzy = True
-
-        info = self.info
-
-        if dayfirst is None:
-            dayfirst = info.dayfirst
-
-        if yearfirst is None:
-            yearfirst = info.yearfirst
-
-        res = self._result()
-        l = _timelex.split(timestr)         # Splits the timestr into tokens
-
-        skipped_idxs = []
-
-        # year/month/day list
-        ymd = _ymd()
-
-        len_l = len(l)
-        i = 0
-        try:
-            while i < len_l:
-
-                # Check if it's a number
-                value_repr = l[i]
-                try:
-                    value = float(value_repr)
-                except ValueError:
-                    value = None
-
-                if value is not None:
-                    # Numeric token
-                    i = self._parse_numeric_token(l, i, info, ymd, res, fuzzy)
-
-                # Check weekday
-                elif info.weekday(l[i]) is not None:
-                    value = info.weekday(l[i])
-                    res.weekday = value
-
-                # Check month name
-                elif info.month(l[i]) is not None:
-                    value = info.month(l[i])
-                    ymd.append(value, 'M')
-
-                    if i + 1 < len_l:
-                        if l[i + 1] in ('-', '/'):
-                            # Jan-01[-99]
-                            sep = l[i + 1]
-                            ymd.append(l[i + 2])
-
-                            if i + 3 < len_l and l[i + 3] == sep:
-                                # Jan-01-99
-                                ymd.append(l[i + 4])
-                                i += 2
-
-                            i += 2
-
-                        elif (i + 4 < len_l and l[i + 1] == l[i + 3] == ' ' and
-                              info.pertain(l[i + 2])):
-                            # Jan of 01
-                            # In this case, 01 is clearly year
-                            if l[i + 4].isdigit():
-                                # Convert it here to become unambiguous
-                                value = int(l[i + 4])
-                                year = str(info.convertyear(value))
-                                ymd.append(year, 'Y')
-                            else:
-                                # Wrong guess
-                                pass
-                                # TODO: not hit in tests
-                            i += 4
-
-                # Check am/pm
-                elif info.ampm(l[i]) is not None:
-                    value = info.ampm(l[i])
-                    val_is_ampm = self._ampm_valid(res.hour, res.ampm, fuzzy)
-
-                    if val_is_ampm:
-                        res.hour = self._adjust_ampm(res.hour, value)
-                        res.ampm = value
-
-                    elif fuzzy:
-                        skipped_idxs.append(i)
-
-                # Check for a timezone name
-                elif self._could_be_tzname(res.hour, res.tzname, res.tzoffset, l[i]):
-                    res.tzname = l[i]
-                    res.tzoffset = info.tzoffset(res.tzname)
-
-                    # Check for something like GMT+3, or BRST+3. Notice
-                    # that it doesn't mean "I am 3 hours after GMT", but
-                    # "my time +3 is GMT". If found, we reverse the
-                    # logic so that timezone parsing code will get it
-                    # right.
-                    if i + 1 < len_l and l[i + 1] in ('+', '-'):
-                        l[i + 1] = ('+', '-')[l[i + 1] == '+']
-                        res.tzoffset = None
-                        if info.utczone(res.tzname):
-                            # With something like GMT+3, the timezone
-                            # is *not* GMT.
-                            res.tzname = None
-
-                # Check for a numbered timezone
-                elif res.hour is not None and l[i] in ('+', '-'):
-                    signal = (-1, 1)[l[i] == '+']
-                    len_li = len(l[i + 1])
-
-                    # TODO: check that l[i + 1] is integer?
-                    if len_li == 4:
-                        # -0300
-                        hour_offset = int(l[i + 1][:2])
-                        min_offset = int(l[i + 1][2:])
-                    elif i + 2 < len_l and l[i + 2] == ':':
-                        # -03:00
-                        hour_offset = int(l[i + 1])
-                        min_offset = int(l[i + 3])  # TODO: Check that l[i+3] is minute-like?
-                        i += 2
-                    elif len_li <= 2:
-                        # -[0]3
-                        hour_offset = int(l[i + 1][:2])
-                        min_offset = 0
-                    else:
-                        raise ValueError(timestr)
-
-                    res.tzoffset = signal * (hour_offset * 3600 + min_offset * 60)
-
-                    # Look for a timezone name between parenthesis
-                    if (i + 5 < len_l and
-                            info.jump(l[i + 2]) and l[i + 3] == '(' and
-                            l[i + 5] == ')' and
-                            3 <= len(l[i + 4]) and
-                            self._could_be_tzname(res.hour, res.tzname,
-                                                  None, l[i + 4])):
-                        # -0300 (BRST)
-                        res.tzname = l[i + 4]
-                        i += 4
-
-                    i += 1
-
-                # Check jumps
-                elif not (info.jump(l[i]) or fuzzy):
-                    raise ValueError(timestr)
-
-                else:
-                    skipped_idxs.append(i)
-                i += 1
-
-            # Process year/month/day
-            year, month, day = ymd.resolve_ymd(yearfirst, dayfirst)
-
-            res.century_specified = ymd.century_specified
-            res.year = year
-            res.month = month
-            res.day = day
-
-        except (IndexError, ValueError):
-            return None, None
-
-        if not info.validate(res):
-            return None, None
-
-        if fuzzy_with_tokens:
-            skipped_tokens = self._recombine_skipped(l, skipped_idxs)
-            return res, tuple(skipped_tokens)
-        else:
-            return res, None
-
-    def _parse_numeric_token(self, tokens, idx, info, ymd, res, fuzzy):
-        # Token is a number
-        value_repr = tokens[idx]
-        try:
-            value = self._to_decimal(value_repr)
-        except Exception as e:
-            six.raise_from(ValueError('Unknown numeric token'), e)
-
-        len_li = len(value_repr)
-
-        len_l = len(tokens)
-
-        if (len(ymd) == 3 and len_li in (2, 4) and
-            res.hour is None and
-            (idx + 1 >= len_l or
-             (tokens[idx + 1] != ':' and
-              info.hms(tokens[idx + 1]) is None))):
-            # 19990101T23[59]
-            s = tokens[idx]
-            res.hour = int(s[:2])
-
-            if len_li == 4:
-                res.minute = int(s[2:])
-
-        elif len_li == 6 or (len_li > 6 and tokens[idx].find('.') == 6):
-            # YYMMDD or HHMMSS[.ss]
-            s = tokens[idx]
-
-            if not ymd and '.' not in tokens[idx]:
-                ymd.append(s[:2])
-                ymd.append(s[2:4])
-                ymd.append(s[4:])
-            else:
-                # 19990101T235959[.59]
-
-                # TODO: Check if res attributes already set.
-                res.hour = int(s[:2])
-                res.minute = int(s[2:4])
-                res.second, res.microsecond = self._parsems(s[4:])
-
-        elif len_li in (8, 12, 14):
-            # YYYYMMDD
-            s = tokens[idx]
-            ymd.append(s[:4], 'Y')
-            ymd.append(s[4:6])
-            ymd.append(s[6:8])
-
-            if len_li > 8:
-                res.hour = int(s[8:10])
-                res.minute = int(s[10:12])
-
-                if len_li > 12:
-                    res.second = int(s[12:])
-
-        elif self._find_hms_idx(idx, tokens, info, allow_jump=True) is not None:
-            # HH[ ]h or MM[ ]m or SS[.ss][ ]s
-            hms_idx = self._find_hms_idx(idx, tokens, info, allow_jump=True)
-            (idx, hms) = self._parse_hms(idx, tokens, info, hms_idx)
-            if hms is not None:
-                # TODO: checking that hour/minute/second are not
-                # already set?
-                self._assign_hms(res, value_repr, hms)
-
-        elif idx + 2 < len_l and tokens[idx + 1] == ':':
-            # HH:MM[:SS[.ss]]
-            res.hour = int(value)
-            value = self._to_decimal(tokens[idx + 2])  # TODO: try/except for this?
-            (res.minute, res.second) = self._parse_min_sec(value)
-
-            if idx + 4 < len_l and tokens[idx + 3] == ':':
-                res.second, res.microsecond = self._parsems(tokens[idx + 4])
-
-                idx += 2
-
-            idx += 2
-
-        elif idx + 1 < len_l and tokens[idx + 1] in ('-', '/', '.'):
-            sep = tokens[idx + 1]
-            ymd.append(value_repr)
-
-            if idx + 2 < len_l and not info.jump(tokens[idx + 2]):
-                if tokens[idx + 2].isdigit():
-                    # 01-01[-01]
-                    ymd.append(tokens[idx + 2])
-                else:
-                    # 01-Jan[-01]
-                    value = info.month(tokens[idx + 2])
-
-                    if value is not None:
-                        ymd.append(value, 'M')
-                    else:
-                        raise ValueError()
-
-                if idx + 3 < len_l and tokens[idx + 3] == sep:
-                    # We have three members
-                    value = info.month(tokens[idx + 4])
-
-                    if value is not None:
-                        ymd.append(value, 'M')
-                    else:
-                        ymd.append(tokens[idx + 4])
-                    idx += 2
-
-                idx += 1
-            idx += 1
-
-        elif idx + 1 >= len_l or info.jump(tokens[idx + 1]):
-            if idx + 2 < len_l and info.ampm(tokens[idx + 2]) is not None:
-                # 12 am
-                hour = int(value)
-                res.hour = self._adjust_ampm(hour, info.ampm(tokens[idx + 2]))
-                idx += 1
-            else:
-                # Year, month or day
-                ymd.append(value)
-            idx += 1
-
-        elif info.ampm(tokens[idx + 1]) is not None and (0 <= value < 24):
-            # 12am
-            hour = int(value)
-            res.hour = self._adjust_ampm(hour, info.ampm(tokens[idx + 1]))
-            idx += 1
-
-        elif ymd.could_be_day(value):
-            ymd.append(value)
-
-        elif not fuzzy:
-            raise ValueError()
-
-        return idx
-
-    def _find_hms_idx(self, idx, tokens, info, allow_jump):
-        len_l = len(tokens)
-
-        if idx+1 < len_l and info.hms(tokens[idx+1]) is not None:
-            # There is an "h", "m", or "s" label following this token.  We take
-            # assign the upcoming label to the current token.
-            # e.g. the "12" in 12h"
-            hms_idx = idx + 1
-
-        elif (allow_jump and idx+2 < len_l and tokens[idx+1] == ' ' and
-              info.hms(tokens[idx+2]) is not None):
-            # There is a space and then an "h", "m", or "s" label.
-            # e.g. the "12" in "12 h"
-            hms_idx = idx + 2
-
-        elif idx > 0 and info.hms(tokens[idx-1]) is not None:
-            # There is a "h", "m", or "s" preceding this token.  Since neither
-            # of the previous cases was hit, there is no label following this
-            # token, so we use the previous label.
-            # e.g. the "04" in "12h04"
-            hms_idx = idx-1
-
-        elif (1 < idx == len_l-1 and tokens[idx-1] == ' ' and
-              info.hms(tokens[idx-2]) is not None):
-            # If we are looking at the final token, we allow for a
-            # backward-looking check to skip over a space.
-            # TODO: Are we sure this is the right condition here?
-            hms_idx = idx - 2
-
-        else:
-            hms_idx = None
-
-        return hms_idx
-
-    def _assign_hms(self, res, value_repr, hms):
-        # See GH issue #427, fixing float rounding
-        value = self._to_decimal(value_repr)
-
-        if hms == 0:
-            # Hour
-            res.hour = int(value)
-            if value % 1:
-                res.minute = int(60*(value % 1))
-
-        elif hms == 1:
-            (res.minute, res.second) = self._parse_min_sec(value)
-
-        elif hms == 2:
-            (res.second, res.microsecond) = self._parsems(value_repr)
-
-    def _could_be_tzname(self, hour, tzname, tzoffset, token):
-        return (hour is not None and
-                tzname is None and
-                tzoffset is None and
-                len(token) <= 5 and
-                (all(x in string.ascii_uppercase for x in token)
-                 or token in self.info.UTCZONE))
-
-    def _ampm_valid(self, hour, ampm, fuzzy):
-        """
-        For fuzzy parsing, 'a' or 'am' (both valid English words)
-        may erroneously trigger the AM/PM flag. Deal with that
-        here.
-        """
-        val_is_ampm = True
-
-        # If there's already an AM/PM flag, this one isn't one.
-        if fuzzy and ampm is not None:
-            val_is_ampm = False
-
-        # If AM/PM is found and hour is not, raise a ValueError
-        if hour is None:
-            if fuzzy:
-                val_is_ampm = False
-            else:
-                raise ValueError('No hour specified with AM or PM flag.')
-        elif not 0 <= hour <= 12:
-            # If AM/PM is found, it's a 12 hour clock, so raise
-            # an error for invalid range
-            if fuzzy:
-                val_is_ampm = False
-            else:
-                raise ValueError('Invalid hour specified for 12-hour clock.')
-
-        return val_is_ampm
-
-    def _adjust_ampm(self, hour, ampm):
-        if hour < 12 and ampm == 1:
-            hour += 12
-        elif hour == 12 and ampm == 0:
-            hour = 0
-        return hour
-
-    def _parse_min_sec(self, value):
-        # TODO: Every usage of this function sets res.second to the return
-        # value. Are there any cases where second will be returned as None and
-        # we *don't* want to set res.second = None?
-        minute = int(value)
-        second = None
-
-        sec_remainder = value % 1
-        if sec_remainder:
-            second = int(60 * sec_remainder)
-        return (minute, second)
-
-    def _parse_hms(self, idx, tokens, info, hms_idx):
-        # TODO: Is this going to admit a lot of false-positives for when we
-        # just happen to have digits and "h", "m" or "s" characters in non-date
-        # text?  I guess hex hashes won't have that problem, but there's plenty
-        # of random junk out there.
-        if hms_idx is None:
-            hms = None
-            new_idx = idx
-        elif hms_idx > idx:
-            hms = info.hms(tokens[hms_idx])
-            new_idx = hms_idx
-        else:
-            # Looking backwards, increment one.
-            hms = info.hms(tokens[hms_idx]) + 1
-            new_idx = idx
-
-        return (new_idx, hms)
-
-    # ------------------------------------------------------------------
-    # Handling for individual tokens.  These are kept as methods instead
-    #  of functions for the sake of customizability via subclassing.
-
-    def _parsems(self, value):
-        """Parse a I[.F] seconds value into (seconds, microseconds)."""
-        if "." not in value:
-            return int(value), 0
-        else:
-            i, f = value.split(".")
-            return int(i), int(f.ljust(6, "0")[:6])
-
-    def _to_decimal(self, val):
-        try:
-            decimal_value = Decimal(val)
-            # See GH 662, edge case, infinite value should not be converted
-            #  via `_to_decimal`
-            if not decimal_value.is_finite():
-                raise ValueError("Converted decimal value is infinite or NaN")
-        except Exception as e:
-            msg = "Could not convert %s to decimal" % val
-            six.raise_from(ValueError(msg), e)
-        else:
-            return decimal_value
-
-    # ------------------------------------------------------------------
-    # Post-Parsing construction of datetime output.  These are kept as
-    #  methods instead of functions for the sake of customizability via
-    #  subclassing.
-
-    def _build_tzinfo(self, tzinfos, tzname, tzoffset):
-        if callable(tzinfos):
-            tzdata = tzinfos(tzname, tzoffset)
-        else:
-            tzdata = tzinfos.get(tzname)
-        # handle case where tzinfo is paased an options that returns None
-        # eg tzinfos = {'BRST' : None}
-        if isinstance(tzdata, datetime.tzinfo) or tzdata is None:
-            tzinfo = tzdata
-        elif isinstance(tzdata, text_type):
-            tzinfo = tz.tzstr(tzdata)
-        elif isinstance(tzdata, integer_types):
-            tzinfo = tz.tzoffset(tzname, tzdata)
-        else:
-            raise TypeError("Offset must be tzinfo subclass, tz string, "
-                            "or int offset.")
-        return tzinfo
-
-    def _build_tzaware(self, naive, res, tzinfos):
-        if (callable(tzinfos) or (tzinfos and res.tzname in tzinfos)):
-            tzinfo = self._build_tzinfo(tzinfos, res.tzname, res.tzoffset)
-            aware = naive.replace(tzinfo=tzinfo)
-            aware = self._assign_tzname(aware, res.tzname)
-
-        elif res.tzname and res.tzname in time.tzname:
-            aware = naive.replace(tzinfo=tz.tzlocal())
-
-            # Handle ambiguous local datetime
-            aware = self._assign_tzname(aware, res.tzname)
-
-            # This is mostly relevant for winter GMT zones parsed in the UK
-            if (aware.tzname() != res.tzname and
-                    res.tzname in self.info.UTCZONE):
-                aware = aware.replace(tzinfo=tz.UTC)
-
-        elif res.tzoffset == 0:
-            aware = naive.replace(tzinfo=tz.UTC)
-
-        elif res.tzoffset:
-            aware = naive.replace(tzinfo=tz.tzoffset(res.tzname, res.tzoffset))
-
-        elif not res.tzname and not res.tzoffset:
-            # i.e. no timezone information was found.
-            aware = naive
-
-        elif res.tzname:
-            # tz-like string was parsed but we don't know what to do
-            # with it
-            warnings.warn("tzname {tzname} identified but not understood.  "
-                          "Pass `tzinfos` argument in order to correctly "
-                          "return a timezone-aware datetime.  In a future "
-                          "version, this will raise an "
-                          "exception.".format(tzname=res.tzname),
-                          category=UnknownTimezoneWarning)
-            aware = naive
-
-        return aware
-
-    def _build_naive(self, res, default):
-        repl = {}
-        for attr in ("year", "month", "day", "hour",
-                     "minute", "second", "microsecond"):
-            value = getattr(res, attr)
-            if value is not None:
-                repl[attr] = value
-
-        if 'day' not in repl:
-            # If the default day exceeds the last day of the month, fall back
-            # to the end of the month.
-            cyear = default.year if res.year is None else res.year
-            cmonth = default.month if res.month is None else res.month
-            cday = default.day if res.day is None else res.day
-
-            if cday > monthrange(cyear, cmonth)[1]:
-                repl['day'] = monthrange(cyear, cmonth)[1]
-
-        naive = default.replace(**repl)
-
-        if res.weekday is not None and not res.day:
-            naive = naive + relativedelta.relativedelta(weekday=res.weekday)
-
-        return naive
-
-    def _assign_tzname(self, dt, tzname):
-        if dt.tzname() != tzname:
-            new_dt = tz.enfold(dt, fold=1)
-            if new_dt.tzname() == tzname:
-                return new_dt
-
-        return dt
-
-    def _recombine_skipped(self, tokens, skipped_idxs):
-        """
-        >>> tokens = ["foo", " ", "bar", " ", "19June2000", "baz"]
-        >>> skipped_idxs = [0, 1, 2, 5]
-        >>> _recombine_skipped(tokens, skipped_idxs)
-        ["foo bar", "baz"]
-        """
-        skipped_tokens = []
-        for i, idx in enumerate(sorted(skipped_idxs)):
-            if i > 0 and idx - 1 == skipped_idxs[i - 1]:
-                skipped_tokens[-1] = skipped_tokens[-1] + tokens[idx]
-            else:
-                skipped_tokens.append(tokens[idx])
-
-        return skipped_tokens
-
-
-DEFAULTPARSER = parser()
-
-
-def parse(timestr, parserinfo=None, **kwargs):
-    """
-
-    Parse a string in one of the supported formats, using the
-    ``parserinfo`` parameters.
-
-    :param timestr:
-        A string containing a date/time stamp.
-
-    :param parserinfo:
-        A :class:`parserinfo` object containing parameters for the parser.
-        If ``None``, the default arguments to the :class:`parserinfo`
-        constructor are used.
-
-    The ``**kwargs`` parameter takes the following keyword arguments:
-
-    :param default:
-        The default datetime object, if this is a datetime object and not
-        ``None``, elements specified in ``timestr`` replace elements in the
-        default object.
-
-    :param ignoretz:
-        If set ``True``, time zones in parsed strings are ignored and a naive
-        :class:`datetime` object is returned.
-
-    :param tzinfos:
-        Additional time zone names / aliases which may be present in the
-        string. This argument maps time zone names (and optionally offsets
-        from those time zones) to time zones. This parameter can be a
-        dictionary with timezone aliases mapping time zone names to time
-        zones or a function taking two parameters (``tzname`` and
-        ``tzoffset``) and returning a time zone.
-
-        The timezones to which the names are mapped can be an integer
-        offset from UTC in seconds or a :class:`tzinfo` object.
-
-        .. doctest::
-           :options: +NORMALIZE_WHITESPACE
-
-            >>> from dateutil.parser import parse
-            >>> from dateutil.tz import gettz
-            >>> tzinfos = {"BRST": -7200, "CST": gettz("America/Chicago")}
-            >>> parse("2012-01-19 17:21:00 BRST", tzinfos=tzinfos)
-            datetime.datetime(2012, 1, 19, 17, 21, tzinfo=tzoffset(u'BRST', -7200))
-            >>> parse("2012-01-19 17:21:00 CST", tzinfos=tzinfos)
-            datetime.datetime(2012, 1, 19, 17, 21,
-                              tzinfo=tzfile('/usr/share/zoneinfo/America/Chicago'))
-
-        This parameter is ignored if ``ignoretz`` is set.
-
-    :param dayfirst:
-        Whether to interpret the first value in an ambiguous 3-integer date
-        (e.g. 01/05/09) as the day (``True``) or month (``False``). If
-        ``yearfirst`` is set to ``True``, this distinguishes between YDM and
-        YMD. If set to ``None``, this value is retrieved from the current
-        :class:`parserinfo` object (which itself defaults to ``False``).
-
-    :param yearfirst:
-        Whether to interpret the first value in an ambiguous 3-integer date
-        (e.g. 01/05/09) as the year. If ``True``, the first number is taken to
-        be the year, otherwise the last number is taken to be the year. If
-        this is set to ``None``, the value is retrieved from the current
-        :class:`parserinfo` object (which itself defaults to ``False``).
-
-    :param fuzzy:
-        Whether to allow fuzzy parsing, allowing for string like "Today is
-        January 1, 2047 at 8:21:00AM".
-
-    :param fuzzy_with_tokens:
-        If ``True``, ``fuzzy`` is automatically set to True, and the parser
-        will return a tuple where the first element is the parsed
-        :class:`datetime.datetime` datetimestamp and the second element is
-        a tuple containing the portions of the string which were ignored:
-
-        .. doctest::
-
-            >>> from dateutil.parser import parse
-            >>> parse("Today is January 1, 2047 at 8:21:00AM", fuzzy_with_tokens=True)
-            (datetime.datetime(2047, 1, 1, 8, 21), (u'Today is ', u' ', u'at '))
-
-    :return:
-        Returns a :class:`datetime.datetime` object or, if the
-        ``fuzzy_with_tokens`` option is ``True``, returns a tuple, the
-        first element being a :class:`datetime.datetime` object, the second
-        a tuple containing the fuzzy tokens.
-
-    :raises ParserError:
-        Raised for invalid or unknown string formats, if the provided
-        :class:`tzinfo` is not in a valid format, or if an invalid date would
-        be created.
-
-    :raises OverflowError:
-        Raised if the parsed date exceeds the largest valid C integer on
-        your system.
-    """
-    if parserinfo:
-        return parser(parserinfo).parse(timestr, **kwargs)
-    else:
-        return DEFAULTPARSER.parse(timestr, **kwargs)
-
-
-class _tzparser(object):
-
-    class _result(_resultbase):
-
-        __slots__ = ["stdabbr", "stdoffset", "dstabbr", "dstoffset",
-                     "start", "end"]
-
-        class _attr(_resultbase):
-            __slots__ = ["month", "week", "weekday",
-                         "yday", "jyday", "day", "time"]
-
-        def __repr__(self):
-            return self._repr("")
-
-        def __init__(self):
-            _resultbase.__init__(self)
-            self.start = self._attr()
-            self.end = self._attr()
-
-    def parse(self, tzstr):
-        res = self._result()
-        l = [x for x in re.split(r'([,:.]|[a-zA-Z]+|[0-9]+)',tzstr) if x]
-        used_idxs = list()
-        try:
-
-            len_l = len(l)
-
-            i = 0
-            while i < len_l:
-                # BRST+3[BRDT[+2]]
-                j = i
-                while j < len_l and not [x for x in l[j]
-                                         if x in "0123456789:,-+"]:
-                    j += 1
-                if j != i:
-                    if not res.stdabbr:
-                        offattr = "stdoffset"
-                        res.stdabbr = "".join(l[i:j])
-                    else:
-                        offattr = "dstoffset"
-                        res.dstabbr = "".join(l[i:j])
-
-                    for ii in range(j):
-                        used_idxs.append(ii)
-                    i = j
-                    if (i < len_l and (l[i] in ('+', '-') or l[i][0] in
-                                       "0123456789")):
-                        if l[i] in ('+', '-'):
-                            # Yes, that's right.  See the TZ variable
-                            # documentation.
-                            signal = (1, -1)[l[i] == '+']
-                            used_idxs.append(i)
-                            i += 1
-                        else:
-                            signal = -1
-                        len_li = len(l[i])
-                        if len_li == 4:
-                            # -0300
-                            setattr(res, offattr, (int(l[i][:2]) * 3600 +
-                                                   int(l[i][2:]) * 60) * signal)
-                        elif i + 1 < len_l and l[i + 1] == ':':
-                            # -03:00
-                            setattr(res, offattr,
-                                    (int(l[i]) * 3600 +
-                                     int(l[i + 2]) * 60) * signal)
-                            used_idxs.append(i)
-                            i += 2
-                        elif len_li <= 2:
-                            # -[0]3
-                            setattr(res, offattr,
-                                    int(l[i][:2]) * 3600 * signal)
-                        else:
-                            return None
-                        used_idxs.append(i)
-                        i += 1
-                    if res.dstabbr:
-                        break
-                else:
-                    break
-
-
-            if i < len_l:
-                for j in range(i, len_l):
-                    if l[j] == ';':
-                        l[j] = ','
-
-                assert l[i] == ','
-
-                i += 1
-
-            if i >= len_l:
-                pass
-            elif (8 <= l.count(',') <= 9 and
-                  not [y for x in l[i:] if x != ','
-                       for y in x if y not in "0123456789+-"]):
-                # GMT0BST,3,0,30,3600,10,0,26,7200[,3600]
-                for x in (res.start, res.end):
-                    x.month = int(l[i])
-                    used_idxs.append(i)
-                    i += 2
-                    if l[i] == '-':
-                        value = int(l[i + 1]) * -1
-                        used_idxs.append(i)
-                        i += 1
-                    else:
-                        value = int(l[i])
-                    used_idxs.append(i)
-                    i += 2
-                    if value:
-                        x.week = value
-                        x.weekday = (int(l[i]) - 1) % 7
-                    else:
-                        x.day = int(l[i])
-                    used_idxs.append(i)
-                    i += 2
-                    x.time = int(l[i])
-                    used_idxs.append(i)
-                    i += 2
-                if i < len_l:
-                    if l[i] in ('-', '+'):
-                        signal = (-1, 1)[l[i] == "+"]
-                        used_idxs.append(i)
-                        i += 1
-                    else:
-                        signal = 1
-                    used_idxs.append(i)
-                    res.dstoffset = (res.stdoffset + int(l[i]) * signal)
-
-                # This was a made-up format that is not in normal use
-                warn(('Parsed time zone "%s"' % tzstr) +
-                     'is in a non-standard dateutil-specific format, which ' +
-                     'is now deprecated; support for parsing this format ' +
-                     'will be removed in future versions. It is recommended ' +
-                     'that you switch to a standard format like the GNU ' +
-                     'TZ variable format.', tz.DeprecatedTzFormatWarning)
-            elif (l.count(',') == 2 and l[i:].count('/') <= 2 and
-                  not [y for x in l[i:] if x not in (',', '/', 'J', 'M',
-                                                     '.', '-', ':')
-                       for y in x if y not in "0123456789"]):
-                for x in (res.start, res.end):
-                    if l[i] == 'J':
-                        # non-leap year day (1 based)
-                        used_idxs.append(i)
-                        i += 1
-                        x.jyday = int(l[i])
-                    elif l[i] == 'M':
-                        # month[-.]week[-.]weekday
-                        used_idxs.append(i)
-                        i += 1
-                        x.month = int(l[i])
-                        used_idxs.append(i)
-                        i += 1
-                        assert l[i] in ('-', '.')
-                        used_idxs.append(i)
-                        i += 1
-                        x.week = int(l[i])
-                        if x.week == 5:
-                            x.week = -1
-                        used_idxs.append(i)
-                        i += 1
-                        assert l[i] in ('-', '.')
-                        used_idxs.append(i)
-                        i += 1
-                        x.weekday = (int(l[i]) - 1) % 7
-                    else:
-                        # year day (zero based)
-                        x.yday = int(l[i]) + 1
-
-                    used_idxs.append(i)
-                    i += 1
-
-                    if i < len_l and l[i] == '/':
-                        used_idxs.append(i)
-                        i += 1
-                        # start time
-                        len_li = len(l[i])
-                        if len_li == 4:
-                            # -0300
-                            x.time = (int(l[i][:2]) * 3600 +
-                                      int(l[i][2:]) * 60)
-                        elif i + 1 < len_l and l[i + 1] == ':':
-                            # -03:00
-                            x.time = int(l[i]) * 3600 + int(l[i + 2]) * 60
-                            used_idxs.append(i)
-                            i += 2
-                            if i + 1 < len_l and l[i + 1] == ':':
-                                used_idxs.append(i)
-                                i += 2
-                                x.time += int(l[i])
-                        elif len_li <= 2:
-                            # -[0]3
-                            x.time = (int(l[i][:2]) * 3600)
-                        else:
-                            return None
-                        used_idxs.append(i)
-                        i += 1
-
-                    assert i == len_l or l[i] == ','
-
-                    i += 1
-
-                assert i >= len_l
-
-        except (IndexError, ValueError, AssertionError):
-            return None
-
-        unused_idxs = set(range(len_l)).difference(used_idxs)
-        res.any_unused_tokens = not {l[n] for n in unused_idxs}.issubset({",",":"})
-        return res
-
-
-DEFAULTTZPARSER = _tzparser()
-
-
-def _parsetz(tzstr):
-    return DEFAULTTZPARSER.parse(tzstr)
-
-
-class ParserError(ValueError):
-    """Exception subclass used for any failure to parse a datetime string.
-
-    This is a subclass of :py:exc:`ValueError`, and should be raised any time
-    earlier versions of ``dateutil`` would have raised ``ValueError``.
-
-    .. versionadded:: 2.8.1
-    """
-    def __str__(self):
-        try:
-            return self.args[0] % self.args[1:]
-        except (TypeError, IndexError):
-            return super(ParserError, self).__str__()
-
-    def __repr__(self):
-        args = ", ".join("'%s'" % arg for arg in self.args)
-        return "%s(%s)" % (self.__class__.__name__, args)
-
-
-class UnknownTimezoneWarning(RuntimeWarning):
-    """Raised when the parser finds a timezone it cannot parse into a tzinfo.
-
-    .. versionadded:: 2.7.0
-    """
-# vim:ts=4:sw=4:et

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/components/radio.py

@@ -1,193 +0,0 @@
-"""gr.Radio() component."""
-
-from __future__ import annotations
-
-from typing import Any, Callable, Literal
-
-from gradio_client.documentation import document, set_documentation_group
-from gradio_client.serializing import StringSerializable
-
-from gradio.components.base import FormComponent, IOComponent, _Keywords
-from gradio.deprecation import warn_deprecation, warn_style_method_deprecation
-from gradio.events import Changeable, EventListenerMethod, Inputable, Selectable
-from gradio.interpretation import NeighborInterpretable
-
-set_documentation_group("component")
-
-
-@document()
-class Radio(
-    FormComponent,
-    Selectable,
-    Changeable,
-    Inputable,
-    IOComponent,
-    StringSerializable,
-    NeighborInterpretable,
-):
-    """
-    Creates a set of radio buttons of which only one can be selected.
-    Preprocessing: passes the value of the selected radio button as a {str} or its index as an {int} into the function, depending on `type`.
-    Postprocessing: expects a {str} corresponding to the value of the radio button to be selected.
-    Examples-format: a {str} representing the radio option to select.
-
-    Demos: sentence_builder, titanic_survival, blocks_essay
-    """
-
-    def __init__(
-        self,
-        choices: list[str] | None = None,
-        *,
-        value: str | Callable | None = None,
-        type: str = "value",
-        label: str | None = None,
-        info: str | None = None,
-        every: float | None = None,
-        show_label: bool | None = None,
-        container: bool = True,
-        scale: int | None = None,
-        min_width: int = 160,
-        interactive: bool | None = None,
-        visible: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            choices: list of options to select from.
-            value: the button selected by default. If None, no button is selected by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            type: Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
-            label: component name in interface.
-            info: additional component description.
-            every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
-            show_label: if True, will display label.
-            container: If True, will place the component in a container - providing some extra padding around the border.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-            interactive: if True, choices in this radio group will be selectable; if False, selection will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
-            visible: If False, component will be hidden.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-        """
-        self.choices = choices or []
-        valid_types = ["value", "index"]
-        if type not in valid_types:
-            raise ValueError(
-                f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}"
-            )
-        self.type = type
-        self.select: EventListenerMethod
-        """
-        Event listener for when the user selects Radio option.
-        Uses event data gradio.SelectData to carry `value` referring to label of selected option, and `index` to refer to index.
-        See EventData documentation on how to use this event data.
-        """
-        IOComponent.__init__(
-            self,
-            label=label,
-            info=info,
-            every=every,
-            show_label=show_label,
-            container=container,
-            scale=scale,
-            min_width=min_width,
-            interactive=interactive,
-            visible=visible,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            value=value,
-            **kwargs,
-        )
-        NeighborInterpretable.__init__(self)
-
-    def get_config(self):
-        return {
-            "choices": self.choices,
-            "value": self.value,
-            **IOComponent.get_config(self),
-        }
-
-    def example_inputs(self) -> dict[str, Any]:
-        return {
-            "raw": self.choices[0] if self.choices else None,
-            "serialized": self.choices[0] if self.choices else None,
-        }
-
-    @staticmethod
-    def update(
-        value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE,
-        choices: list[str] | None = None,
-        label: str | None = None,
-        info: str | None = None,
-        show_label: bool | None = None,
-        container: bool | None = None,
-        scale: int | None = None,
-        min_width: int | None = None,
-        interactive: bool | None = None,
-        visible: bool | None = None,
-    ):
-        return {
-            "choices": choices,
-            "label": label,
-            "info": info,
-            "show_label": show_label,
-            "container": container,
-            "scale": scale,
-            "min_width": min_width,
-            "interactive": interactive,
-            "visible": visible,
-            "value": value,
-            "__type__": "update",
-        }
-
-    def preprocess(self, x: str | None) -> str | int | None:
-        """
-        Parameters:
-            x: selected choice
-        Returns:
-            selected choice as string or index within choice list
-        """
-        if self.type == "value":
-            return x
-        elif self.type == "index":
-            if x is None:
-                return None
-            else:
-                return self.choices.index(x)
-        else:
-            raise ValueError(
-                f"Unknown type: {self.type}. Please choose from: 'value', 'index'."
-            )
-
-    def get_interpretation_neighbors(self, x):
-        choices = list(self.choices)
-        choices.remove(x)
-        return choices, {}
-
-    def get_interpretation_scores(
-        self, x, neighbors, scores: list[float | None], **kwargs
-    ) -> list:
-        """
-        Returns:
-            Each value represents the interpretation score corresponding to each choice.
-        """
-        scores.insert(self.choices.index(x), None)
-        return scores
-
-    def style(
-        self,
-        *,
-        item_container: bool | None = None,
-        container: bool | None = None,
-        **kwargs,
-    ):
-        """
-        This method is deprecated. Please set these arguments in the constructor instead.
-        """
-        warn_style_method_deprecation()
-        if item_container is not None:
-            warn_deprecation("The `item_container` parameter is deprecated.")
-        if container is not None:
-            self.container = container
-        return self

spaces/Defalt-404/Bittensor_Explore/README.md

@@ -1,12 +0,0 @@
----
-title: Bittensor Explore
-emoji: ⚡
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Detomo/ai-comic-generation/src/app/engine/forbidden.ts

@@ -1,6 +0,0 @@
-
-// the NSFW has to contain bad words, but doing so might get the code flagged
-// or attract unwanted attention, so we hash them
-export const forbidden = [
-  // TODO implement this
-]

spaces/DragGan/DragGan-Inversion/PTI/models/StyleCLIP/models/stylegan2/model.py

@@ -1,674 +0,0 @@
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from  models.StyleCLIP.models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d
-
-
-class PixelNorm(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, input):
-        return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_kernel(k):
-    k = torch.tensor(k, dtype=torch.float32)
-
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]
-
-    k /= k.sum()
-
-    return k
-
-
-class Upsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel) * (factor ** 2)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2 + factor - 1
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
-
-        return out
-
-
-class Downsample(nn.Module):
-    def __init__(self, kernel, factor=2):
-        super().__init__()
-
-        self.factor = factor
-        kernel = make_kernel(kernel)
-        self.register_buffer('kernel', kernel)
-
-        p = kernel.shape[0] - factor
-
-        pad0 = (p + 1) // 2
-        pad1 = p // 2
-
-        self.pad = (pad0, pad1)
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
-
-        return out
-
-
-class Blur(nn.Module):
-    def __init__(self, kernel, pad, upsample_factor=1):
-        super().__init__()
-
-        kernel = make_kernel(kernel)
-
-        if upsample_factor > 1:
-            kernel = kernel * (upsample_factor ** 2)
-
-        self.register_buffer('kernel', kernel)
-
-        self.pad = pad
-
-    def forward(self, input):
-        out = upfirdn2d(input, self.kernel, pad=self.pad)
-
-        return out
-
-
-class EqualConv2d(nn.Module):
-    def __init__(
-        self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channel, in_channel, kernel_size, kernel_size)
-        )
-        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
-
-        self.stride = stride
-        self.padding = padding
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channel))
-
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        out = F.conv2d(
-            input,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
-            f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
-        )
-
-
-class EqualLinear(nn.Module):
-    def __init__(
-        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
-    ):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
-
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
-
-        else:
-            self.bias = None
-
-        self.activation = activation
-
-        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
-        self.lr_mul = lr_mul
-
-    def forward(self, input):
-        if self.activation:
-            out = F.linear(input, self.weight * self.scale)
-            out = fused_leaky_relu(out, self.bias * self.lr_mul)
-
-        else:
-            out = F.linear(
-                input, self.weight * self.scale, bias=self.bias * self.lr_mul
-            )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})'
-        )
-
-
-class ScaledLeakyReLU(nn.Module):
-    def __init__(self, negative_slope=0.2):
-        super().__init__()
-
-        self.negative_slope = negative_slope
-
-    def forward(self, input):
-        out = F.leaky_relu(input, negative_slope=self.negative_slope)
-
-        return out * math.sqrt(2)
-
-
-class ModulatedConv2d(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        demodulate=True,
-        upsample=False,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-    ):
-        super().__init__()
-
-        self.eps = 1e-8
-        self.kernel_size = kernel_size
-        self.in_channel = in_channel
-        self.out_channel = out_channel
-        self.upsample = upsample
-        self.downsample = downsample
-
-        if upsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) - (kernel_size - 1)
-            pad0 = (p + 1) // 2 + factor - 1
-            pad1 = p // 2 + 1
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            self.blur = Blur(blur_kernel, pad=(pad0, pad1))
-
-        fan_in = in_channel * kernel_size ** 2
-        self.scale = 1 / math.sqrt(fan_in)
-        self.padding = kernel_size // 2
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
-        )
-
-        self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
-
-        self.demodulate = demodulate
-
-    def __repr__(self):
-        return (
-            f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, '
-            f'upsample={self.upsample}, downsample={self.downsample})'
-        )
-
-    def forward(self, input, style):
-        batch, in_channel, height, width = input.shape
-
-        style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
-        weight = self.scale * self.weight * style
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
-            weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
-
-        weight = weight.view(
-            batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
-        )
-
-        if self.upsample:
-            input = input.view(1, batch * in_channel, height, width)
-            weight = weight.view(
-                batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-            out = self.blur(out)
-
-        elif self.downsample:
-            input = self.blur(input)
-            _, _, height, width = input.shape
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        else:
-            input = input.view(1, batch * in_channel, height, width)
-            out = F.conv2d(input, weight, padding=self.padding, groups=batch)
-            _, _, height, width = out.shape
-            out = out.view(batch, self.out_channel, height, width)
-
-        return out
-
-
-class NoiseInjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.zeros(1))
-
-    def forward(self, image, noise=None):
-        if noise is None:
-            batch, _, height, width = image.shape
-            noise = image.new_empty(batch, 1, height, width).normal_()
-
-        return image + self.weight * noise
-
-
-class ConstantInput(nn.Module):
-    def __init__(self, channel, size=4):
-        super().__init__()
-
-        self.input = nn.Parameter(torch.randn(1, channel, size, size))
-
-    def forward(self, input):
-        batch = input.shape[0]
-        out = self.input.repeat(batch, 1, 1, 1)
-
-        return out
-
-
-class StyledConv(nn.Module):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        style_dim,
-        upsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        demodulate=True,
-    ):
-        super().__init__()
-
-        self.conv = ModulatedConv2d(
-            in_channel,
-            out_channel,
-            kernel_size,
-            style_dim,
-            upsample=upsample,
-            blur_kernel=blur_kernel,
-            demodulate=demodulate,
-        )
-
-        self.noise = NoiseInjection()
-        # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
-        # self.activate = ScaledLeakyReLU(0.2)
-        self.activate = FusedLeakyReLU(out_channel)
-
-    def forward(self, input, style, noise=None):
-        out = self.conv(input, style)
-        out = self.noise(out, noise=noise)
-        # out = out + self.bias
-        out = self.activate(out)
-
-        return out
-
-
-class ToRGB(nn.Module):
-    def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        if upsample:
-            self.upsample = Upsample(blur_kernel)
-
-        self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, input, style, skip=None):
-        out = self.conv(input, style)
-        out = out + self.bias
-
-        if skip is not None:
-            skip = self.upsample(skip)
-
-            out = out + skip
-
-        return out
-
-
-class Generator(nn.Module):
-    def __init__(
-        self,
-        size,
-        style_dim,
-        n_mlp,
-        channel_multiplier=2,
-        blur_kernel=[1, 3, 3, 1],
-        lr_mlp=0.01,
-    ):
-        super().__init__()
-
-        self.size = size
-
-        self.style_dim = style_dim
-
-        layers = [PixelNorm()]
-
-        for i in range(n_mlp):
-            layers.append(
-                EqualLinear(
-                    style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu'
-                )
-            )
-
-        self.style = nn.Sequential(*layers)
-
-        self.channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        self.input = ConstantInput(self.channels[4])
-        self.conv1 = StyledConv(
-            self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
-        )
-        self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
-
-        self.log_size = int(math.log(size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-
-        self.convs = nn.ModuleList()
-        self.upsamples = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channel = self.channels[4]
-
-        for layer_idx in range(self.num_layers):
-            res = (layer_idx + 5) // 2
-            shape = [1, 1, 2 ** res, 2 ** res]
-            self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape))
-
-        for i in range(3, self.log_size + 1):
-            out_channel = self.channels[2 ** i]
-
-            self.convs.append(
-                StyledConv(
-                    in_channel,
-                    out_channel,
-                    3,
-                    style_dim,
-                    upsample=True,
-                    blur_kernel=blur_kernel,
-                )
-            )
-
-            self.convs.append(
-                StyledConv(
-                    out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
-                )
-            )
-
-            self.to_rgbs.append(ToRGB(out_channel, style_dim))
-
-            in_channel = out_channel
-
-        self.n_latent = self.log_size * 2 - 2
-
-    def make_noise(self):
-        device = self.input.input.device
-
-        noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
-
-        return noises
-
-    def mean_latent(self, n_latent):
-        latent_in = torch.randn(
-            n_latent, self.style_dim, device=self.input.input.device
-        )
-        latent = self.style(latent_in).mean(0, keepdim=True)
-
-        return latent
-
-    def get_latent(self, input):
-        return self.style(input)
-
-    def forward(
-        self,
-        styles,
-        return_latents=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-    ):
-        if not input_is_latent:
-            styles = [self.style(s) for s in styles]
-
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers
-            else:
-                noise = [
-                    getattr(self.noises, f'noise_{i}') for i in range(self.num_layers)
-                ]
-
-        if truncation < 1:
-            style_t = []
-
-            for style in styles:
-                style_t.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-
-            styles = style_t
-
-        if len(styles) < 2:
-            inject_index = self.n_latent
-
-            if styles[0].ndim < 3:
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
-            else:
-                latent = styles[0]
-
-        else:
-            if inject_index is None:
-                inject_index = random.randint(1, self.n_latent - 1)
-
-            latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
-
-            latent = torch.cat([latent, latent2], 1)
-
-        out = self.input(latent)
-        out = self.conv1(out, latent[:, 0], noise=noise[0])
-
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-
-        else:
-            return image, None
-
-
-class ConvLayer(nn.Sequential):
-    def __init__(
-        self,
-        in_channel,
-        out_channel,
-        kernel_size,
-        downsample=False,
-        blur_kernel=[1, 3, 3, 1],
-        bias=True,
-        activate=True,
-    ):
-        layers = []
-
-        if downsample:
-            factor = 2
-            p = (len(blur_kernel) - factor) + (kernel_size - 1)
-            pad0 = (p + 1) // 2
-            pad1 = p // 2
-
-            layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
-
-            stride = 2
-            self.padding = 0
-
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-
-        layers.append(
-            EqualConv2d(
-                in_channel,
-                out_channel,
-                kernel_size,
-                padding=self.padding,
-                stride=stride,
-                bias=bias and not activate,
-            )
-        )
-
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channel))
-
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super().__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        self.conv1 = ConvLayer(in_channel, in_channel, 3)
-        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
-
-        self.skip = ConvLayer(
-            in_channel, out_channel, 1, downsample=True, activate=False, bias=False
-        )
-
-    def forward(self, input):
-        out = self.conv1(input)
-        out = self.conv2(out)
-
-        skip = self.skip(input)
-        out = (out + skip) / math.sqrt(2)
-
-        return out
-
-
-class Discriminator(nn.Module):
-    def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
-        super().__init__()
-
-        channels = {
-            4: 512,
-            8: 512,
-            16: 512,
-            32: 512,
-            64: 256 * channel_multiplier,
-            128: 128 * channel_multiplier,
-            256: 64 * channel_multiplier,
-            512: 32 * channel_multiplier,
-            1024: 16 * channel_multiplier,
-        }
-
-        convs = [ConvLayer(3, channels[size], 1)]
-
-        log_size = int(math.log(size, 2))
-
-        in_channel = channels[size]
-
-        for i in range(log_size, 2, -1):
-            out_channel = channels[2 ** (i - 1)]
-
-            convs.append(ResBlock(in_channel, out_channel, blur_kernel))
-
-            in_channel = out_channel
-
-        self.convs = nn.Sequential(*convs)
-
-        self.stddev_group = 4
-        self.stddev_feat = 1
-
-        self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
-        self.final_linear = nn.Sequential(
-            EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'),
-            EqualLinear(channels[4], 1),
-        )
-
-    def forward(self, input):
-        out = self.convs(input)
-
-        batch, channel, height, width = out.shape
-        group = min(batch, self.stddev_group)
-        stddev = out.view(
-            group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
-        )
-        stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
-        stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
-        stddev = stddev.repeat(group, 1, height, width)
-        out = torch.cat([out, stddev], 1)
-
-        out = self.final_conv(out)
-
-        out = out.view(batch, -1)
-        out = self.final_linear(out)
-
-        return out
-